import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

pd.set_option('display.max_rows', 111)
pd.set_option('display.max_columns', 111)

data = pd.read_csv('train.csv')

data

print("Anzahl der Zeilen im Datensatz : ", len(data))
print("Anzahl der Spalten im Datensatz : ", len(data.columns))

Anzahl der Zeilen im Datensatz :  129156
Anzahl der Spalten im Datensatz :  20

#Variablentypen
data.dtypes

Id                                       int64
Location                                object
Weekly Cases                           float64
Year                                     int64
Weekly Cases per Million               float64
Weekly Deaths                          float64
Weekly Deaths per Million              float64
Total Vaccinations                     float64
People Vaccinated                      float64
People Fully Vaccinated                float64
Total Boosters                         float64
Daily Vaccinations                     float64
Total Vaccinations per Hundred         float64
People Vaccinated per Hundred          float64
People Fully Vaccinated per Hundred    float64
Total Boosters per Hundred             float64
Daily Vaccinations per Hundred         float64
Daily People Vaccinated                float64
Daily People Vaccinated per Hundred    float64
Next Week's Deaths                     float64
dtype: object

print(data.dtypes.value_counts())
data.dtypes.value_counts().plot(kind='pie', legend=True, figsize=(8,5))

float64    17
int64       2
object      1
Name: count, dtype: int64

<Axes: ylabel='count'>

#Descriptive Analyse
data.describe()

#Analyse fehlender Werte
import missingno as msno
plt.figure(figsize=(20, 8))
sns.heatmap(data.isna(), cbar=False)

msno.matrix(data)

<Axes: >

# Prozentzahl der Felhlenden Werte
missing_rate = (data.isna().sum()/data.shape[0])*100
print(missing_rate.sort_values())
missing_rate.sort_values().plot.bar(rot=90, figsize=(10,6), color = 'red')

Id                                      0.000000
Year                                    0.000000
Next Week's Deaths                      0.000000
Location                                0.000000
Weekly Cases                            0.164917
Weekly Cases per Million                0.672055
Weekly Deaths                           0.974016
Weekly Deaths per Million               1.478058
Daily Vaccinations per Hundred         39.731797
Daily Vaccinations                     39.731797
Daily People Vaccinated                40.079439
Daily People Vaccinated per Hundred    40.079439
Total Vaccinations per Hundred         68.820651
Total Vaccinations                     68.820651
People Vaccinated                      70.246059
People Vaccinated per Hundred          70.246059
People Fully Vaccinated per Hundred    71.682307
People Fully Vaccinated                71.682307
Total Boosters                         84.633312
Total Boosters per Hundred             84.633312
dtype: float64

<Axes: >

#Die Spalten, die mehr als 60% der Zeilen enthalten, die NaN Werte enthalten.
missing_groesser_60 = data.columns[missing_rate > 60]
print("Mehr als 60% der Zeilen enthalten Nullwerte\n\n",missing_groesser_60)

print("\n********************************************************************************")
missing_zwischen_39_40 = data.columns[(missing_rate > 38) & (missing_rate < 41)]
print("\nBis 40% der Zeilen enthalten Nullwerte\n\n", missing_zwischen_39_40)

print("\n********************************************************************************")
missing_sehr_klein = data.columns[missing_rate < 1.5]
print("\nEnthalten fast keine Null-Werte\n\n", missing_sehr_klein)

Mehr als 60% der Zeilen enthalten Nullwerte

 Index(['Total Vaccinations', 'People Vaccinated', 'People Fully Vaccinated',
       'Total Boosters', 'Total Vaccinations per Hundred',
       'People Vaccinated per Hundred', 'People Fully Vaccinated per Hundred',
       'Total Boosters per Hundred'],
      dtype='object')

********************************************************************************

Bis 40% der Zeilen enthalten Nullwerte

 Index(['Daily Vaccinations', 'Daily Vaccinations per Hundred',
       'Daily People Vaccinated', 'Daily People Vaccinated per Hundred'],
      dtype='object')

********************************************************************************

Enthalten fast keine Null-Werte

 Index(['Id', 'Location', 'Weekly Cases', 'Year', 'Weekly Cases per Million',
       'Weekly Deaths', 'Weekly Deaths per Million', 'Next Week's Deaths'],
      dtype='object')

# BOXPLOT DER SPALTEN
# for col in data.columns: 
#     if data[col].dtype in ['int64', 'float64']:  
#         Q1 = data[col].quantile(0.25)  # Erstes Quartil
#         Q3 = data[col].quantile(0.75)  # Drittes Quartil
#         median = data[col].median() 
        
#         plt.figure(figsize=(8, 5))  
#         sns.boxplot(data[col], boxprops=dict(facecolor='orange', edgecolor='black'))  
#         plt.title(f'{col}', fontsize=14)  
        
#         # Hinzufügen der statistischen Informationen als text.
#         plt.xlabel(f'Q1: {Q1:.2f}, Median: {median:.2f}, Q3: {Q3:.2f}', fontsize=12)
#         plt.ylabel('Werte', fontsize=12)
        
#         plt.show()  

cols_per_row = 2
num_cols = len([col for col in data.drop(["Location", "Id"], axis=1).columns] )
rows = (num_cols + cols_per_row - 1) // cols_per_row  #Berechnung der Anzahl von erforderlichen Zeilen

fig, axes = plt.subplots(rows, cols_per_row, figsize=(12, 5 * rows))
axes = axes.flatten() 

for i, col in enumerate(data.drop(["Location", "Id"], axis=1).columns):
        Q1 = data[col].quantile(0.25)  # erste Quartile
        Q3 = data[col].quantile(0.75)  # dritte Quartile
        median = data[col].median()  # Median

        sns.boxplot(ax=axes[i], x=data[col], boxprops=dict(facecolor='orange', edgecolor='black'))
        axes[i].set_title(f'{col}', fontsize=14)
        axes[i].set_xlabel(f'Q1: {Q1:.2f}, Median: {median:.2f}, Q3: {Q3:.2f}', fontsize=10)
        axes[i].set_ylabel('')

#Unnötige Achsen ausblenden, wenn die Spaltenanzahl ungerade ist
for j in range(num_cols, len(axes)):
    axes[j].set_visible(False)


plt.tight_layout()
plt.show()

# HISTOGRAMME DER VARIABLEN

cols_per_row = 2
num_cols = len([col for col in data.drop(["Location", "Id"], axis=1).columns] )
rows = (num_cols + cols_per_row - 1) // cols_per_row  #Berechnung der Anzahl von erforderlichen Zeilen

fig, axes = plt.subplots(rows, cols_per_row, figsize=(12, 5 * rows))
axes = axes.flatten() 

for i, col in enumerate(data.drop(["Location", "Id"], axis=1).columns) : 
        sns.histplot(ax = axes[i], x = data[col], bins=50, kde=True,  color='chocolate')
        axes[i].set_title(f'{col}', fontsize=14)

#Unnötige Achsen ausblenden, wenn die Spaltenanzahl ungerade ist
for j in range(num_cols, len(axes)):
    axes[j].set_visible(False)
    
plt.tight_layout()
plt.show()

# SKEWNESS DER VARIABLEN
skewdata = data.drop("Location", axis=1)
skewness = skewdata.skew()
print(f"Skewness der Variablen(Spalten) :\n{skewness}" )

Skewness der Variablen(Spalten) :
Id                                     -0.000918
Weekly Cases                           17.334228
Year                                    0.139635
Weekly Cases per Million                7.806967
Weekly Deaths                           9.096170
Weekly Deaths per Million               8.303667
Total Vaccinations                      7.061840
People Vaccinated                       6.846799
People Fully Vaccinated                 7.143399
Total Boosters                          6.990664
Daily Vaccinations                      9.282717
Total Vaccinations per Hundred          0.518582
People Vaccinated per Hundred          -0.023551
People Fully Vaccinated per Hundred     0.150049
Total Boosters per Hundred              0.934193
Daily Vaccinations per Hundred          5.123234
Daily People Vaccinated                10.693151
Daily People Vaccinated per Hundred    13.667387
Next Week's Deaths                      9.087042
dtype: float64

plt.figure(figsize=(8,5))
sns.barplot(x = skewness.index, y = skewness.values, color='green')
plt.title("Skewness plot der Attributen")
plt.xticks(rotation=90, ha='right')
plt.show()

# PROBABILITY PLOT
import scipy.stats as stats

cols_per_row = 2
num_cols = len([col for col in data.drop(["Location", "Id"], axis=1).columns] )
rows = (num_cols + cols_per_row - 1) // cols_per_row  #Berechnung der Anzahl von erforderlichen Zeilen

fig, axes = plt.subplots(rows, cols_per_row, figsize=(12, 5 * rows))
axes = axes.flatten() 

for i, col in enumerate(data.drop(["Location", "Id"], axis=1).columns) :

        stats.probplot(data[col], dist="norm", plot=axes[i])
        axes[i].set_title(f'{col}', fontsize=14)

#Unnötige Achsen ausblenden, wenn die Spaltenanzahl ungerade ist
for j in range(num_cols, len(axes)):
    axes[j].set_visible(False)
        
plt.tight_layout()
plt.show()

data

unab_variablen = data.drop(["Id", "Next Week's Deaths"], axis=1)
target = data["Next Week's Deaths"]

cols_per_row = 2
num_cols = len([col for col in unab_variablen.columns] )
rows = (num_cols + cols_per_row - 1) // cols_per_row  #Berechnung der Anzahl von erforderlichen Zeilen

fig, axes = plt.subplots(rows, cols_per_row, figsize=(12, 5 * rows))
axes = axes.flatten() 

for i, col in enumerate(unab_variablen.columns):

    sns.scatterplot(ax = axes[i], x=unab_variablen[col], y=target)
    axes[i].set_title(f"{col} vs. Next Week's Deaths")
    axes[i].set_xlabel(f"{col}")
    axes[i].set_ylabel("Next Week's Deaths")

#Unnötige Achsen ausblenden, wenn die Spaltenanzahl ungerade ist
for j in range(num_cols, len(axes)):
    axes[j].set_visible(False)

plt.tight_layout()
plt.show()

correlation_matrix = data.drop('Location',axis=1).corr()
correlations_with_target = correlation_matrix["Next Week's Deaths"].drop("Next Week's Deaths")
plt.figure(figsize=(10,5))
sns.barplot(x= correlations_with_target.index, y=correlations_with_target.values, color = 'violet')
plt.title("Korrelationsplot Der Variablen gegen Next Week's Deaths")
plt.xticks(rotation=90, ha='right')
plt.show()

#Copie der Originalen datensatz
df = data.copy()

# Löschen der NaN-Wette, weil sns.pairplot() ist sehr zeitaufwendig, insbesondere wenn es viele Zeilen gibt.
df.dropna(axis=0, inplace=True)
#df.drop("Id", axis=1, inplace=True)
df.head()

#Darstellung der Korrelation
sns.pairplot(data=df, hue='Year')

<seaborn.axisgrid.PairGrid at 0x13a9ef5bfd0>

plt.figure(figsize=(12,8))
sns.heatmap(df.drop('Location',axis=1).corr(), annot=True, cbar=False)

<Axes: >

data

#Anzahl der wöchentlichen Fälle und Tode
annual_trends = data.groupby('Year')[["Weekly Cases", "Weekly Deaths"]].sum()
annual_trends

annual_trends.plot(kind='bar', title='Jährliche Evolution der Fälle und des Tods', figsize=(8,5))

<Axes: title={'center': 'Jährliche Evolution der Fälle und des Tods'}, xlabel='Year'>

# Regionen mit höherer Todrate und Krankheitsrate
regional_totals = data.groupby('Location')[['Weekly Cases', 'Weekly Deaths']].sum()

plt.figure(figsize=(14, 7))

#Erste
plt.subplot(1, 2, 1)
regional_totals.sort_values(by='Weekly Cases', ascending=False).head(15).plot(kind='bar', title='Die 15 Regionen mit den meisten Krankheitsfällen', ax=plt.gca())
plt.xticks(rotation=45, ha='right')  

# Zweite
plt.subplot(1, 2, 2)
regional_totals.sort_values(by='Weekly Deaths', ascending=False).head(15).plot(kind='bar', title='Die 15 Regionen mit den meisten Todesfällen', ax=plt.gca())
plt.xticks(rotation=45, ha='right') 

plt.tight_layout()
plt.show()

import geopandas as gpd
countries_path = "ne_110m_admin_0_countries/ne_110m_admin_0_countries.shp"
world = gpd.read_file(countries_path)
#print(world['GEOUNIT'][4])
world.loc[4, 'GEOUNIT'] = 'United States' #um den gleichen Name der Vereinigten Staaten wie in meinem Datensatz zu haben.

continents_path = "shapefiles/world-continents.shp"
continents = gpd.read_file(continents_path)
continents.rename(columns={'geometry': 'continent_geometry'}, inplace=True)

#world.head(5)
continents

#Gesamtzahl der Krankheitsfälle per Region.
regional_cases = data.groupby('Location')['Weekly Cases'].sum().reset_index()

world_cases = world.merge(regional_cases, left_on='GEOUNIT', right_on='Location', how='outer')
world_cases['Location'] = world_cases['GEOUNIT'].combine_first(world_cases['Location'])

world_cases = world_cases.merge(continents, left_on='Location', right_on='CONTINENT', how='outer')
world_cases['geometry'] = world_cases['geometry'].combine_first(world_cases['continent_geometry'])
world_cases['Location'] = world_cases['Location'].combine_first(world_cases['CONTINENT_y'])

world_cases.head(3)
#world_cases[world_cases["Location"].duplicated()]
#world_cases = world_cases.drop_duplicates(subset=['GEOUNIT'])

ax = world_cases.plot(column='Weekly Cases', cmap='Wistia', legend=True, figsize=(15, 10))

threshold = pow(10, 7.69)

for _, row in world_cases.iterrows():
    if row['Weekly Cases'] > threshold: 
        if row['geometry'] is not None:
            ax.annotate(
                text=row['Location'], 
                xy=(row['geometry'].centroid.x, row['geometry'].centroid.y),
                fontsize=8,
                color='black',
                fontweight='bold'
            )
        

ax.set_title('Verteilung der wöchentlichen COVID-19-Fälle weltweit(Top Länder)', fontsize=16)
ax.set_xlabel('Längengrad')
ax.set_ylabel('Breitengrad')

plt.show()

#Gesamtzahl der Todfälle per Region.
regional_deaths = data.groupby('Location')['Weekly Deaths'].sum().reset_index()

world_deaths = world_cases.merge(regional_deaths, left_on='Location', right_on='Location', how='outer')
world_deaths.head(3)

ax = world_deaths.plot(column='Weekly Deaths', cmap='Wistia', legend=True, figsize=(15, 10))

threshold = pow(10, 5.60)

for _, row in world_deaths.iterrows():
    if row['Weekly Deaths'] > threshold:  
        if row['geometry'] is not None:
            ax.annotate(
                text=row['Location'], 
                xy=(row['geometry'].centroid.x, row['geometry'].centroid.y),
                fontsize=6.8,
                color='black',
                fontweight='bold'
            )        

ax.set_title('Verteilung der wöchentlichen COVID-19-Todfälle weltweit', fontsize=16)
ax.set_xlabel('Längengrad')
ax.set_ylabel('Breitengrad')

plt.show()

# Die top 16 Regionen(oder Länder) mit höheren Impfungsraten
data_grouped_top = data.groupby(["Location"]).sum().sort_values(by = "Total Vaccinations", ascending=False).head(16)
data_grouped_top

#Namen der 16 top Länder mit höheren Impfquoten
data_grouped_top_index = data_grouped_top.index
data_grouped_top_index

Index(['World', 'Asia', 'Upper middle income', 'China', 'Lower middle income',
       'High income', 'India', 'Europe', 'North America', 'European Union',
       'South America', 'United States', 'Africa', 'Brazil', 'Japan',
       'Indonesia'],
      dtype='object', name='Location')

#Estellen eines Dataframes mit Namen der Länder als index
data_location_as_index = data.set_index("Location")
data_location_as_index.head(4)

# Plot des Histograms der Spalten 'Total Vaccinations' und 'Weekly Cases' von den top 16 Ländern(Oder Regionen)
for location in data_grouped_top_index:
    plt.figure(figsize=(14,7))

    plt.subplot(1, 2, 1)
    sns.histplot(data_location_as_index["Total Vaccinations"][location], label='Total Vaccinations', kde=True, color='orange')
    plt.title(f'{location}', fontweight='bold')
    plt.legend()

    plt.subplot(1, 2, 2)
    sns.histplot(data_location_as_index["Weekly Cases"][location], label='Weekly Cases', kde=True)
    plt.title(f'{location}', fontweight='bold')
    plt.legend()

    
    plt.show()

#Beweisen des Nicht-Zusammenhang zwischen 'Total Vaccinations' und 'Weekly Cases'
from scipy.stats import pearsonr

df_clean = data.dropna(subset=['Weekly Cases', 'Weekly Deaths', 'Total Vaccinations'])

# Analyser der Korrelation zwischen Impfungen und Krankheitsfällen
corr_cases, _ = pearsonr(df_clean['Total Vaccinations'], df_clean['Weekly Cases'])
corr_deaths, _ = pearsonr(df_clean['Total Vaccinations'], df_clean['Weekly Deaths'])


print(f"Korrelation zwischen Total Vaccinations und Weekly Cases : {corr_cases}")
print(f"Korrelation zwischen Total Vaccinations und Weekly Deaths  : {corr_deaths}")

Korrelation zwischen Total Vaccinations und Weekly Cases : 0.5945225142453268
Korrelation zwischen Total Vaccinations und Weekly Deaths  : 0.42208650052500496

# Plot des Histograms der Spalten 'Total Vaccinations' und 'Weekly Deaths' von den top 16 Ländern(Oder Regionen)
for location in data_grouped_top_index:
    plt.figure(figsize=(14,7))

    plt.subplot(1, 2, 1)
    sns.histplot(data_location_as_index["Total Vaccinations"][location], label='Total Vaccinations', kde=True, color='orange')
    plt.title(f'{location}', fontweight='bold')
    plt.legend()

    plt.subplot(1, 2, 2)
    sns.histplot(data_location_as_index["Weekly Deaths"][location], label='Weekly Deaths', kde=True)
    plt.title(f'{location}', fontweight='bold')
    plt.legend()

    
    plt.show()

def hypo_preprocessing(data, column_names, year=2021, percentage=0.20):
  
    # Wörterbücher zum Speichern der Ergebnisse
    top_laender_dict = {}
    down_laender_dict = {}
    top_down_laender_dict = {}
    
    for column_name in column_names:
        # Extraktion der Daten für das angegebene Jahr
        data_year = data[data['Year'] == year]
        
        # Berechnung der Anzahl der Regionen (Prozentsatz der Regionen)
        n = int(len(data_year["Location"].unique()) * percentage)
        
        # Top- und Down-Regionen für die angegebene Spalte
        data_year_down = data_year.groupby("Location").sum().nsmallest(n, column_name)
        data_year_top = data_year.groupby("Location").sum().nlargest(n, column_name)
        
        # Extrahieren der Zeilen für die Top- und Down-Regionen
        data_down = data.loc[(data["Location"].isin(data_year_down.index)) & (data["Year"] == year)]
        data_top = data.loc[(data["Location"].isin(data_year_top.index)) & (data["Year"] == year)]
        
        # Reinigung und Erstellung eines kombinierten DataFrames
        data_down = data_down.dropna(axis=0, subset=["Weekly Cases", "Weekly Deaths", "Next Week's Deaths", column_name])
        data_top = data_top.dropna(axis=0, subset=["Weekly Cases", "Weekly Deaths", "Next Week's Deaths", column_name])
        data_top_down = pd.concat([data_down, data_top], ignore_index=True)

        #Die beiden DataFrames ausbalancieren
        if data_top.shape[0] > data_down.shape[0] : 
            data_top = data_top.sample(data_down.shape[0])
        else : 
            data_down = data_down.sample(data_top.shape[0])

        #crer un dataframe
        
        # Speicherung in Wörterbüchern
        top_laender_dict[column_name] = data_top
        down_laender_dict[column_name] = data_down
        top_down_laender_dict[column_name] = data_top_down
        
        print(f'\n\n***** {column_name.upper()} *****\n')
        print(f'data_top enthält jetzt : {data_top.shape[0]} lignes et {data_top.shape[1]} colonnes')
        print(f'data_down enthält jetzt : {data_down.shape[0]} lignes et {data_down.shape[1]} colonnes \n')
        print(f'TOP {len(data_year_top.index)} REGIONEN MIT EINER HÖHEREN ZAHL VON {column_name.upper()} IM JAHR {year}:\n\n{data_year_top.index}\n')
        print(f'DOWN {len(data_year_down.index)} REGIONEN MIT EINER NIEDRIGEREN ZAHL VON {column_name.upper()} IM JAHR {year}:\n\n{data_year_down.index}')
    
    return top_laender_dict, down_laender_dict, top_down_laender_dict

# Definition der Test funktion
from scipy.stats import ttest_ind

def ttest1(columns, hypothese_columns, groupe_1_dict, groupe_2_dict, year=2021):
    alpha = 0.05
    for hypo_col in hypothese_columns: 
        print(f"\n\n HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN UNTERSCHIED IM MITTELWERT DER VARIABLEN ZWISCHEN DEN GRUPPEN MIT HOHEM UND NIEDRIGEM '{hypo_col.upper()}' ({year}) \n\n ")
        for col in columns:
            stat, p = ttest_ind(groupe_1_dict[hypo_col][col], groupe_2_dict[hypo_col][col])
            if p < alpha : 
                print(f'{col :-<70} H0 GEWORFEN\n')
            else: print(f'{col :-<70} 0')

# Definition der Test funktion
from scipy.stats import ttest_ind

def spearmantest(columns, hypothese_columns, groupe_dict, year="Gesamtdaten"):
    alpha = 0.05
    for hypo_col in hypothese_columns: 
        print(f"\n\n HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN MONOTONEN ZUSAMMENHANG ZWISCHEN '{hypo_col.upper()}' UND DEN ANDEREN VARIABLEN ({year}) \n\n ")
        for col in columns:
            if col != hypo_col:
                coef, p = spearmanr(groupe_dict[hypo_col][hypo_col], groupe_dict[hypo_col][col])
                if p < alpha:
                    print(f'{hypo_col} und {col :-<70} H0 GEWORFEN (coef={coef:.3f}, p={p:.3f})\n')
                else:
                    print(f' {hypo_col} und {col :-<70} H0 BEHALTEN (coef={coef:.3f}, p={p:.3f})\n')

        print("\n\n****************************************************************************************************")

#Aufruf von hypo_preprocessing für 2020 Daten
column_names = ["Total Vaccinations", "Weekly Deaths", "Weekly Cases"]
data_top_dict_2020, data_down_dict_2020, data_top_down_dict_2020 = hypo_preprocessing(data, column_names, year=2020)

data_top_down_dict_2020['Weekly Deaths'].head()


***** TOTAL VACCINATIONS *****

data_top enthält jetzt : 2 lignes et 20 colonnes
data_down enthält jetzt : 2 lignes et 20 colonnes 

TOP 40 REGIONEN MIT EINER HÖHEREN ZAHL VON TOTAL VACCINATIONS IM JAHR 2020:

Index(['High income', 'World', 'Asia', 'United States', 'Upper middle income',
       'North America', 'China', 'Israel', 'European Union', 'Europe',
       'Germany', 'Canada', 'Bahrain', 'South America', 'Russia', 'Denmark',
       'Italy', 'Mexico', 'Poland', 'Argentina', 'Slovenia', 'Lithuania',
       'Switzerland', 'Romania', 'Oman', 'Chile', 'Czechia', 'Estonia',
       'Norway', 'France', 'Latvia', 'Greece', 'Luxembourg', 'Kuwait',
       'Bulgaria', 'Ireland', 'Hungary', 'Belgium', 'Singapore',
       'Afghanistan'],
      dtype='object', name='Location')

DOWN 40 REGIONEN MIT EINER NIEDRIGEREN ZAHL VON TOTAL VACCINATIONS IM JAHR 2020:

Index(['Afghanistan', 'Africa', 'Albania', 'Algeria', 'Andorra', 'Angola',
       'Antigua and Barbuda', 'Armenia', 'Aruba', 'Australia', 'Austria',
       'Azerbaijan', 'Bahamas', 'Bangladesh', 'Barbados', 'Belarus', 'Belize',
       'Benin', 'Bermuda', 'Bolivia', 'Bonaire Sint Eustatius and Saba',
       'Bosnia and Herzegovina', 'Botswana', 'Brazil',
       'British Virgin Islands', 'Brunei', 'Burkina Faso', 'Burundi',
       'Cameroon', 'Cape Verde', 'Cayman Islands', 'Central African Republic',
       'Chad', 'Colombia', 'Comoros', 'Congo', 'Costa Rica', 'Cote d'Ivoire',
       'Croatia', 'Cuba'],
      dtype='object', name='Location')


***** WEEKLY DEATHS *****

data_top enthält jetzt : 6837 lignes et 20 colonnes
data_down enthält jetzt : 6837 lignes et 20 colonnes 

TOP 40 REGIONEN MIT EINER HÖHEREN ZAHL VON WEEKLY DEATHS IM JAHR 2020:

Index(['World', 'High income', 'Upper middle income', 'North America',
       'Europe', 'South America', 'European Union', 'United States', 'Asia',
       'Lower middle income', 'Brazil', 'India', 'Mexico', 'Peru',
       'United Kingdom', 'Italy', 'Africa', 'France', 'Iran', 'Russia',
       'Spain', 'Argentina', 'Colombia', 'Poland', 'Germany', 'South Africa',
       'Indonesia', 'Turkey', 'Ukraine', 'Belgium', 'Romania', 'Canada',
       'Chile', 'Iraq', 'Netherlands', 'Ecuador', 'Czechia', 'Low income',
       'Pakistan', 'Hungary'],
      dtype='object', name='Location')

DOWN 40 REGIONEN MIT EINER NIEDRIGEREN ZAHL VON WEEKLY DEATHS IM JAHR 2020:

Index(['British Virgin Islands', 'Eritrea', 'Mongolia', 'Montserrat',
       'Burundi', 'Fiji', 'Cayman Islands', 'Brunei',
       'Bonaire Sint Eustatius and Saba', 'Antigua and Barbuda', 'Saint Lucia',
       'Barbados', 'Gibraltar', 'Monaco', 'Turks and Caicos Islands',
       'Mauritius', 'Bermuda', 'Taiwan', 'Papua New Guinea', 'Comoros',
       'Curacao', 'International', 'Sao Tome and Principe', 'Tanzania',
       'New Zealand', 'Iceland', 'Vietnam', 'Isle of Man', 'Singapore',
       'Liechtenstein', 'Botswana', 'Benin', 'Guinea-Bissau', 'Aruba',
       'Maldives', 'Lesotho', 'San Marino', 'South Sudan', 'Thailand',
       'Gabon'],
      dtype='object', name='Location')


***** WEEKLY CASES *****

data_top enthält jetzt : 6843 lignes et 20 colonnes
data_down enthält jetzt : 6843 lignes et 20 colonnes 

TOP 40 REGIONEN MIT EINER HÖHEREN ZAHL VON WEEKLY CASES IM JAHR 2020:

Index(['World', 'High income', 'Upper middle income', 'North America',
       'Europe', 'Asia', 'United States', 'Lower middle income',
       'European Union', 'South America', 'India', 'Brazil', 'Russia',
       'France', 'Africa', 'United Kingdom', 'Italy', 'Spain', 'Colombia',
       'Argentina', 'Germany', 'Poland', 'Turkey', 'Mexico', 'Iran', 'Ukraine',
       'South Africa', 'Peru', 'Indonesia', 'Netherlands', 'Czechia',
       'Romania', 'Belgium', 'Chile', 'Canada', 'Iraq', 'Bangladesh',
       'Philippines', 'Morocco', 'Switzerland'],
      dtype='object', name='Location')

DOWN 40 REGIONEN MIT EINER NIEDRIGEREN ZAHL VON WEEKLY CASES IM JAHR 2020:

Index(['Montserrat', 'Fiji', 'Brunei', 'British Virgin Islands',
       'Antigua and Barbuda', 'Bonaire Sint Eustatius and Saba', 'Saint Lucia',
       'Mongolia', 'Eritrea', 'Cayman Islands', 'Barbados', 'International',
       'Isle of Man', 'Tanzania', 'Mauritius', 'Bermuda', 'Comoros',
       'Papua New Guinea', 'Burundi', 'Taiwan', 'Monaco',
       'Turks and Caicos Islands', 'Sao Tome and Principe', 'Vietnam',
       'Gibraltar', 'Liberia', 'New Zealand', 'Liechtenstein', 'Yemen',
       'San Marino', 'Chad', 'Guinea-Bissau', 'Sierra Leone', 'Lesotho',
       'Niger', 'South Sudan', 'Benin', 'Togo', 'Curacao', 'Gambia'],
      dtype='object', name='Location')

#Anwendung des T-Tests für 2020
ttest1(list(data.drop(["Id", "Location"], axis=1).columns), ["Total Vaccinations", "Weekly Deaths", "Weekly Cases"],
       data_top_dict_2020, data_down_dict_2020, year=2020)


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN UNTERSCHIED IM MITTELWERT DER VARIABLEN ZWISCHEN DEN GRUPPEN MIT HOHEM UND NIEDRIGEM 'TOTAL VACCINATIONS' (2020) 

 
Weekly Cases---------------------------------------------------------- 0
Year------------------------------------------------------------------ 0
Weekly Cases per Million---------------------------------------------- 0
Weekly Deaths--------------------------------------------------------- 0
Weekly Deaths per Million--------------------------------------------- 0
Total Vaccinations---------------------------------------------------- 0
People Vaccinated----------------------------------------------------- 0
People Fully Vaccinated----------------------------------------------- 0
Total Boosters-------------------------------------------------------- 0
Daily Vaccinations---------------------------------------------------- 0
Total Vaccinations per Hundred---------------------------------------- 0
People Vaccinated per Hundred----------------------------------------- 0
People Fully Vaccinated per Hundred----------------------------------- 0
Total Boosters per Hundred-------------------------------------------- 0
Daily Vaccinations per Hundred---------------------------------------- 0
Daily People Vaccinated----------------------------------------------- 0
Daily People Vaccinated per Hundred----------------------------------- 0
Next Week's Deaths---------------------------------------------------- 0


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN UNTERSCHIED IM MITTELWERT DER VARIABLEN ZWISCHEN DEN GRUPPEN MIT HOHEM UND NIEDRIGEM 'WEEKLY DEATHS' (2020) 

 
Weekly Cases---------------------------------------------------------- H0 GEWORFEN

Year------------------------------------------------------------------ 0
Weekly Cases per Million---------------------------------------------- 0
Weekly Deaths--------------------------------------------------------- H0 GEWORFEN

Weekly Deaths per Million--------------------------------------------- 0
Total Vaccinations---------------------------------------------------- 0
People Vaccinated----------------------------------------------------- 0
People Fully Vaccinated----------------------------------------------- 0
Total Boosters-------------------------------------------------------- 0
Daily Vaccinations---------------------------------------------------- 0
Total Vaccinations per Hundred---------------------------------------- 0
People Vaccinated per Hundred----------------------------------------- 0
People Fully Vaccinated per Hundred----------------------------------- 0
Total Boosters per Hundred-------------------------------------------- 0
Daily Vaccinations per Hundred---------------------------------------- 0
Daily People Vaccinated----------------------------------------------- 0
Daily People Vaccinated per Hundred----------------------------------- 0
Next Week's Deaths---------------------------------------------------- H0 GEWORFEN


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN UNTERSCHIED IM MITTELWERT DER VARIABLEN ZWISCHEN DEN GRUPPEN MIT HOHEM UND NIEDRIGEM 'WEEKLY CASES' (2020) 

 
Weekly Cases---------------------------------------------------------- H0 GEWORFEN

Year------------------------------------------------------------------ 0
Weekly Cases per Million---------------------------------------------- 0
Weekly Deaths--------------------------------------------------------- H0 GEWORFEN

Weekly Deaths per Million--------------------------------------------- 0
Total Vaccinations---------------------------------------------------- 0
People Vaccinated----------------------------------------------------- 0
People Fully Vaccinated----------------------------------------------- 0
Total Boosters-------------------------------------------------------- 0
Daily Vaccinations---------------------------------------------------- 0
Total Vaccinations per Hundred---------------------------------------- 0
People Vaccinated per Hundred----------------------------------------- 0
People Fully Vaccinated per Hundred----------------------------------- 0
Total Boosters per Hundred-------------------------------------------- 0
Daily Vaccinations per Hundred---------------------------------------- 0
Daily People Vaccinated----------------------------------------------- 0
Daily People Vaccinated per Hundred----------------------------------- 0
Next Week's Deaths---------------------------------------------------- H0 GEWORFEN

C:\Users\kalil\anaconda3\Lib\site-packages\scipy\stats\_axis_nan_policy.py:523: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.
  res = hypotest_fun_out(*samples, **kwds)

#Anwendung des Spearmann-Tests für 2020,
from scipy.stats import spearmanr
spearmantest(list(data.drop(["Id", "Location"], axis=1).columns), ["Total Vaccinations", "Weekly Deaths", "Weekly Cases"],
       data_top_down_dict_2020, year="2020")


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN MONOTONEN ZUSAMMENHANG ZWISCHEN 'TOTAL VACCINATIONS' UND DEN ANDEREN VARIABLEN (2020) 

 
Total Vaccinations und Weekly Cases---------------------------------------------------------- H0 GEWORFEN (coef=0.315, p=0.000)

 Total Vaccinations und Year------------------------------------------------------------------ H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Weekly Cases per Million---------------------------------------------- H0 BEHALTEN (coef=-0.089, p=0.131)

Total Vaccinations und Weekly Deaths--------------------------------------------------------- H0 GEWORFEN (coef=0.250, p=0.000)

Total Vaccinations und Weekly Deaths per Million--------------------------------------------- H0 GEWORFEN (coef=-0.167, p=0.004)

 Total Vaccinations und People Vaccinated----------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und People Fully Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Total Boosters-------------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Daily Vaccinations---------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

Total Vaccinations und Total Vaccinations per Hundred---------------------------------------- H0 GEWORFEN (coef=0.757, p=0.000)

 Total Vaccinations und People Vaccinated per Hundred----------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und People Fully Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Total Boosters per Hundred-------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Daily Vaccinations per Hundred---------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Daily People Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Daily People Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

Total Vaccinations und Next Week's Deaths---------------------------------------------------- H0 GEWORFEN (coef=0.239, p=0.000)


****************************************************************************************************


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN MONOTONEN ZUSAMMENHANG ZWISCHEN 'WEEKLY DEATHS' UND DEN ANDEREN VARIABLEN (2020) 

 
Weekly Deaths und Weekly Cases---------------------------------------------------------- H0 GEWORFEN (coef=0.949, p=0.000)

 Weekly Deaths und Year------------------------------------------------------------------ H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Weekly Cases per Million---------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Weekly Deaths per Million--------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Total Vaccinations---------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und People Vaccinated----------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und People Fully Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Total Boosters-------------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Daily Vaccinations---------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Total Vaccinations per Hundred---------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und People Vaccinated per Hundred----------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und People Fully Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Total Boosters per Hundred-------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Daily Vaccinations per Hundred---------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Daily People Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Daily People Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

Weekly Deaths und Next Week's Deaths---------------------------------------------------- H0 GEWORFEN (coef=0.974, p=0.000)


****************************************************************************************************


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN MONOTONEN ZUSAMMENHANG ZWISCHEN 'WEEKLY CASES' UND DEN ANDEREN VARIABLEN (2020) 

 
 Weekly Cases und Year------------------------------------------------------------------ H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Weekly Cases per Million---------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

Weekly Cases und Weekly Deaths--------------------------------------------------------- H0 GEWORFEN (coef=0.951, p=0.000)

 Weekly Cases und Weekly Deaths per Million--------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Total Vaccinations---------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und People Vaccinated----------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und People Fully Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Total Boosters-------------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Daily Vaccinations---------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Total Vaccinations per Hundred---------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und People Vaccinated per Hundred----------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und People Fully Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Total Boosters per Hundred-------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Daily Vaccinations per Hundred---------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Daily People Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Daily People Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

Weekly Cases und Next Week's Deaths---------------------------------------------------- H0 GEWORFEN (coef=0.952, p=0.000)


****************************************************************************************************

C:\Users\kalil\anaconda3\Lib\site-packages\scipy\stats\_stats_py.py:5445: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.
  warnings.warn(stats.ConstantInputWarning(warn_msg))

#Aufruf von hypo_preprocessing für 2021 Daten
column_names = ["Total Vaccinations", "Weekly Deaths", "Weekly Cases"]
data_top_dict_2021, data_down_dict_2021, data_top_down_dict_2021 = hypo_preprocessing(data, column_names, year=2021)

data_top_down_dict_2021['Weekly Deaths'].head()


***** TOTAL VACCINATIONS *****

data_top enthält jetzt : 1033 lignes et 20 colonnes
data_down enthält jetzt : 1033 lignes et 20 colonnes 

TOP 43 REGIONEN MIT EINER HÖHEREN ZAHL VON TOTAL VACCINATIONS IM JAHR 2021:

Index(['World', 'Asia', 'Upper middle income', 'China', 'Lower middle income',
       'High income', 'Europe', 'India', 'North America', 'European Union',
       'United States', 'South America', 'Brazil', 'Indonesia', 'Japan',
       'Africa', 'Germany', 'United Kingdom', 'Turkey', 'France', 'Russia',
       'Italy', 'Mexico', 'Canada', 'South Korea', 'Argentina', 'Spain',
       'Thailand', 'Vietnam', 'Saudi Arabia', 'Chile', 'Malaysia', 'Poland',
       'Pakistan', 'Oceania', 'Colombia', 'Philippines', 'Bangladesh', 'Peru',
       'Low income', 'Australia', 'Cambodia', 'Sri Lanka'],
      dtype='object', name='Location')

DOWN 43 REGIONEN MIT EINER NIEDRIGEREN ZAHL VON TOTAL VACCINATIONS IM JAHR 2021:

Index(['Eritrea', 'International', 'Bonaire Sint Eustatius and Saba',
       'Burundi', 'Montserrat', 'Greenland', 'Anguilla', 'Wallis and Futuna',
       'Djibouti', 'Turks and Caicos Islands', 'British Virgin Islands',
       'Monaco', 'Dominica', 'Vanuatu', 'Andorra', 'Saint Kitts and Nevis',
       'Sao Tome and Principe', 'Grenada', 'Saint Vincent and the Grenadines',
       'Central African Republic', 'Bermuda', 'Haiti', 'Papua New Guinea',
       'Yemen', 'San Marino', 'New Caledonia', 'South Sudan', 'Burkina Faso',
       'Sierra Leone', 'Gambia', 'Lesotho', 'Liberia', 'Gabon', 'Chad',
       'Democratic Republic of Congo', 'Comoros', 'Guinea-Bissau', 'Bahamas',
       'Antigua and Barbuda', 'Faeroe Islands', 'Madagascar',
       'French Polynesia', 'Tanzania'],
      dtype='object', name='Location')


***** WEEKLY DEATHS *****

data_top enthält jetzt : 10703 lignes et 20 colonnes
data_down enthält jetzt : 10703 lignes et 20 colonnes 

TOP 43 REGIONEN MIT EINER HÖHEREN ZAHL VON WEEKLY DEATHS IM JAHR 2021:

Index(['World', 'Upper middle income', 'High income', 'Europe', 'Asia',
       'Lower middle income', 'South America', 'North America',
       'European Union', 'United States', 'Brazil', 'India', 'Russia',
       'Mexico', 'Africa', 'Indonesia', 'Peru', 'Colombia', 'Germany',
       'United Kingdom', 'Ukraine', 'Argentina', 'Iran', 'Poland',
       'South Africa', 'Italy', 'Turkey', 'France', 'Philippines', 'Romania',
       'Spain', 'Vietnam', 'Hungary', 'Malaysia', 'Low income', 'Czechia',
       'Bulgaria', 'Chile', 'Bangladesh', 'Thailand', 'Tunisia', 'Pakistan',
       'Kazakhstan'],
      dtype='object', name='Location')

DOWN 43 REGIONEN MIT EINER NIEDRIGEREN ZAHL VON WEEKLY DEATHS IM JAHR 2021:

Index(['Greenland', 'International', 'Montserrat', 'Vanuatu', 'Bhutan',
       'Anguilla', 'China', 'Wallis and Futuna', 'Iceland', 'Cayman Islands',
       'Faeroe Islands', 'Turks and Caicos Islands',
       'Bonaire Sint Eustatius and Saba', 'New Zealand', 'Liechtenstein',
       'Saint Kitts and Nevis', 'Burundi', 'Monaco',
       'Central African Republic', 'British Virgin Islands', 'Tajikistan',
       'San Marino', 'Isle of Man', 'Sao Tome and Principe', 'Dominica',
       'Sierra Leone', 'Nicaragua', 'Andorra', 'Hong Kong', 'Eritrea',
       'South Sudan', 'Chad', 'Saint Vincent and the Grenadines',
       'Equatorial Guinea', 'Gibraltar', 'Guinea-Bissau', 'Brunei', 'Bermuda',
       'Antigua and Barbuda', 'Timor', 'Djibouti', 'Benin', 'Seychelles'],
      dtype='object', name='Location')


***** WEEKLY CASES *****

data_top enthält jetzt : 10596 lignes et 20 colonnes
data_down enthält jetzt : 10596 lignes et 20 colonnes 

TOP 43 REGIONEN MIT EINER HÖHEREN ZAHL VON WEEKLY CASES IM JAHR 2021:

Index(['World', 'High income', 'Asia', 'Upper middle income', 'Europe',
       'Lower middle income', 'North America', 'European Union',
       'United States', 'South America', 'India', 'Brazil', 'United Kingdom',
       'Russia', 'Turkey', 'France', 'Africa', 'Germany', 'Iran', 'Argentina',
       'Spain', 'Indonesia', 'Italy', 'Colombia', 'Poland', 'Ukraine',
       'Philippines', 'Mexico', 'South Africa', 'Malaysia', 'Netherlands',
       'Thailand', 'Czechia', 'Vietnam', 'Canada', 'Iraq', 'Japan', 'Belgium',
       'Peru', 'Chile', 'Slovakia', 'Romania', 'Low income'],
      dtype='object', name='Location')

DOWN 43 REGIONEN MIT EINER NIEDRIGEREN ZAHL VON WEEKLY CASES IM JAHR 2021:

Index(['International', 'Vanuatu', 'Montserrat', 'Wallis and Futuna',
       'Greenland', 'Anguilla', 'Bhutan', 'Turks and Caicos Islands',
       'Sao Tome and Principe', 'British Virgin Islands',
       'Saint Kitts and Nevis', 'Bonaire Sint Eustatius and Saba', 'Hong Kong',
       'Guinea-Bissau', 'Liechtenstein', 'Chad', 'Antigua and Barbuda',
       'Monaco', 'Tajikistan', 'Sierra Leone', 'Liberia', 'Niger', 'Bermuda',
       'Comoros', 'Faeroe Islands', 'San Marino',
       'Saint Vincent and the Grenadines', 'Dominica',
       'Central African Republic', 'Gambia', 'Grenada', 'Eritrea', 'Gibraltar',
       'Djibouti', 'Yemen', 'Equatorial Guinea', 'Nicaragua', 'Cayman Islands',
       'Burkina Faso', 'New Zealand', 'South Sudan', 'New Caledonia', 'Mali'],
      dtype='object', name='Location')

#Anwendung des T-Tests für 2021
ttest1(list(data.drop(["Id", "Location"], axis=1).columns), ["Total Vaccinations", "Weekly Deaths", "Weekly Cases"],
       data_top_dict_2021, data_down_dict_2021, year=2021)


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN UNTERSCHIED IM MITTELWERT DER VARIABLEN ZWISCHEN DEN GRUPPEN MIT HOHEM UND NIEDRIGEM 'TOTAL VACCINATIONS' (2021) 

 
Weekly Cases---------------------------------------------------------- H0 GEWORFEN

Year------------------------------------------------------------------ 0
Weekly Cases per Million---------------------------------------------- H0 GEWORFEN

Weekly Deaths--------------------------------------------------------- H0 GEWORFEN

Weekly Deaths per Million--------------------------------------------- 0
Total Vaccinations---------------------------------------------------- H0 GEWORFEN

People Vaccinated----------------------------------------------------- 0
People Fully Vaccinated----------------------------------------------- 0
Total Boosters-------------------------------------------------------- 0
Daily Vaccinations---------------------------------------------------- 0
Total Vaccinations per Hundred---------------------------------------- H0 GEWORFEN

People Vaccinated per Hundred----------------------------------------- 0
People Fully Vaccinated per Hundred----------------------------------- 0
Total Boosters per Hundred-------------------------------------------- 0
Daily Vaccinations per Hundred---------------------------------------- 0
Daily People Vaccinated----------------------------------------------- 0
Daily People Vaccinated per Hundred----------------------------------- 0
Next Week's Deaths---------------------------------------------------- H0 GEWORFEN


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN UNTERSCHIED IM MITTELWERT DER VARIABLEN ZWISCHEN DEN GRUPPEN MIT HOHEM UND NIEDRIGEM 'WEEKLY DEATHS' (2021) 

 
Weekly Cases---------------------------------------------------------- H0 GEWORFEN

Year------------------------------------------------------------------ 0
Weekly Cases per Million---------------------------------------------- 0
Weekly Deaths--------------------------------------------------------- H0 GEWORFEN

Weekly Deaths per Million--------------------------------------------- 0
Total Vaccinations---------------------------------------------------- 0
People Vaccinated----------------------------------------------------- 0
People Fully Vaccinated----------------------------------------------- 0
Total Boosters-------------------------------------------------------- 0
Daily Vaccinations---------------------------------------------------- 0
Total Vaccinations per Hundred---------------------------------------- 0
People Vaccinated per Hundred----------------------------------------- 0
People Fully Vaccinated per Hundred----------------------------------- 0
Total Boosters per Hundred-------------------------------------------- 0
Daily Vaccinations per Hundred---------------------------------------- 0
Daily People Vaccinated----------------------------------------------- 0
Daily People Vaccinated per Hundred----------------------------------- 0
Next Week's Deaths---------------------------------------------------- H0 GEWORFEN


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN UNTERSCHIED IM MITTELWERT DER VARIABLEN ZWISCHEN DEN GRUPPEN MIT HOHEM UND NIEDRIGEM 'WEEKLY CASES' (2021) 

 
Weekly Cases---------------------------------------------------------- H0 GEWORFEN

Year------------------------------------------------------------------ 0
Weekly Cases per Million---------------------------------------------- 0
Weekly Deaths--------------------------------------------------------- H0 GEWORFEN

Weekly Deaths per Million--------------------------------------------- 0
Total Vaccinations---------------------------------------------------- 0
People Vaccinated----------------------------------------------------- 0
People Fully Vaccinated----------------------------------------------- 0
Total Boosters-------------------------------------------------------- 0
Daily Vaccinations---------------------------------------------------- 0
Total Vaccinations per Hundred---------------------------------------- 0
People Vaccinated per Hundred----------------------------------------- 0
People Fully Vaccinated per Hundred----------------------------------- 0
Total Boosters per Hundred-------------------------------------------- 0
Daily Vaccinations per Hundred---------------------------------------- 0
Daily People Vaccinated----------------------------------------------- 0
Daily People Vaccinated per Hundred----------------------------------- 0
Next Week's Deaths---------------------------------------------------- H0 GEWORFEN

C:\Users\kalil\anaconda3\Lib\site-packages\scipy\stats\_axis_nan_policy.py:523: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.
  res = hypotest_fun_out(*samples, **kwds)

#Anwendung des Spearmann-Tests für 2021,
from scipy.stats import spearmanr
spearmantest(list(data.drop(["Id", "Location"], axis=1).columns), ["Total Vaccinations", "Weekly Deaths", "Weekly Cases"],
       data_top_down_dict_2021, year="2021")


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN MONOTONEN ZUSAMMENHANG ZWISCHEN 'TOTAL VACCINATIONS' UND DEN ANDEREN VARIABLEN (2021) 

 
Total Vaccinations und Weekly Cases---------------------------------------------------------- H0 GEWORFEN (coef=0.612, p=0.000)

 Total Vaccinations und Year------------------------------------------------------------------ H0 BEHALTEN (coef=nan, p=nan)

Total Vaccinations und Weekly Cases per Million---------------------------------------------- H0 GEWORFEN (coef=0.131, p=0.000)

Total Vaccinations und Weekly Deaths--------------------------------------------------------- H0 GEWORFEN (coef=0.543, p=0.000)

Total Vaccinations und Weekly Deaths per Million--------------------------------------------- H0 GEWORFEN (coef=0.128, p=0.000)

 Total Vaccinations und People Vaccinated----------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und People Fully Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Total Boosters-------------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Daily Vaccinations---------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

Total Vaccinations und Total Vaccinations per Hundred---------------------------------------- H0 GEWORFEN (coef=0.561, p=0.000)

 Total Vaccinations und People Vaccinated per Hundred----------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und People Fully Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Total Boosters per Hundred-------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Daily Vaccinations per Hundred---------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Daily People Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Daily People Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

Total Vaccinations und Next Week's Deaths---------------------------------------------------- H0 GEWORFEN (coef=0.540, p=0.000)


****************************************************************************************************


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN MONOTONEN ZUSAMMENHANG ZWISCHEN 'WEEKLY DEATHS' UND DEN ANDEREN VARIABLEN (2021) 

 
Weekly Deaths und Weekly Cases---------------------------------------------------------- H0 GEWORFEN (coef=0.936, p=0.000)

 Weekly Deaths und Year------------------------------------------------------------------ H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Weekly Cases per Million---------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Weekly Deaths per Million--------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Total Vaccinations---------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und People Vaccinated----------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und People Fully Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Total Boosters-------------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Daily Vaccinations---------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Total Vaccinations per Hundred---------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und People Vaccinated per Hundred----------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und People Fully Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Total Boosters per Hundred-------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Daily Vaccinations per Hundred---------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Daily People Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Daily People Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

Weekly Deaths und Next Week's Deaths---------------------------------------------------- H0 GEWORFEN (coef=0.962, p=0.000)


****************************************************************************************************


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN MONOTONEN ZUSAMMENHANG ZWISCHEN 'WEEKLY CASES' UND DEN ANDEREN VARIABLEN (2021) 

 
 Weekly Cases und Year------------------------------------------------------------------ H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Weekly Cases per Million---------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

Weekly Cases und Weekly Deaths--------------------------------------------------------- H0 GEWORFEN (coef=0.942, p=0.000)

 Weekly Cases und Weekly Deaths per Million--------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Total Vaccinations---------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und People Vaccinated----------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und People Fully Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Total Boosters-------------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Daily Vaccinations---------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Total Vaccinations per Hundred---------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und People Vaccinated per Hundred----------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und People Fully Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Total Boosters per Hundred-------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Daily Vaccinations per Hundred---------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Daily People Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Daily People Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

Weekly Cases und Next Week's Deaths---------------------------------------------------- H0 GEWORFEN (coef=0.944, p=0.000)


****************************************************************************************************

C:\Users\kalil\anaconda3\Lib\site-packages\scipy\stats\_stats_py.py:5445: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.
  warnings.warn(stats.ConstantInputWarning(warn_msg))

#Aufruf von hypo_preprocessing für 2022 Daten
column_names = ["Total Vaccinations", "Weekly Deaths", "Weekly Cases"]
data_top_dict_2022, data_down_dict_2022, data_top_down_dict_2022 = hypo_preprocessing(data, column_names, year=2022)

data_top_down_dict_2022['Weekly Deaths'].head()


***** TOTAL VACCINATIONS *****

data_top enthält jetzt : 439 lignes et 20 colonnes
data_down enthält jetzt : 439 lignes et 20 colonnes 

TOP 44 REGIONEN MIT EINER HÖHEREN ZAHL VON TOTAL VACCINATIONS IM JAHR 2022:

Index(['World', 'Asia', 'Upper middle income', 'Lower middle income', 'China',
       'High income', 'India', 'Europe', 'North America', 'European Union',
       'South America', 'United States', 'Africa', 'Brazil', 'Japan',
       'Indonesia', 'Germany', 'France', 'United Kingdom', 'Low income',
       'Vietnam', 'Pakistan', 'Italy', 'Thailand', 'Turkey', 'South Korea',
       'Russia', 'Bangladesh', 'Argentina', 'Mexico', 'Canada', 'Iran',
       'Oceania', 'Peru', 'Malaysia', 'Chile', 'Australia', 'Poland', 'Taiwan',
       'Philippines', 'Cuba', 'Ecuador', 'Colombia', 'Spain'],
      dtype='object', name='Location')

DOWN 44 REGIONEN MIT EINER NIEDRIGEREN ZAHL VON TOTAL VACCINATIONS IM JAHR 2022:

Index(['Bonaire Sint Eustatius and Saba', 'Cook Islands', 'Eritrea',
       'International', 'Monaco', 'North Korea', 'Palau',
       'Saint Pierre and Miquelon', 'Montserrat', 'Burundi',
       'Wallis and Futuna', 'Turks and Caicos Islands', 'Anguilla',
       'Saint Kitts and Nevis', 'British Virgin Islands', 'Kiribati',
       'Greenland', 'Sao Tome and Principe', 'Vanuatu', 'Tonga', 'San Marino',
       'Antigua and Barbuda', 'Lesotho', 'Faeroe Islands', 'Macao', 'Bermuda',
       'Andorra', 'Cayman Islands', 'Bosnia and Herzegovina', 'Dominica',
       'Djibouti', 'Grenada', 'Gambia', 'Seychelles',
       'Saint Vincent and the Grenadines', 'Comoros', 'Samoa', 'Bahamas',
       'Liechtenstein', 'Equatorial Guinea', 'Solomon Islands', 'Saint Lucia',
       'Namibia', 'Haiti'],
      dtype='object', name='Location')


***** WEEKLY DEATHS *****

data_top enthält jetzt : 5527 lignes et 20 colonnes
data_down enthält jetzt : 5527 lignes et 20 colonnes 

TOP 44 REGIONEN MIT EINER HÖHEREN ZAHL VON WEEKLY DEATHS IM JAHR 2022:

Index(['World', 'High income', 'Europe', 'Upper middle income',
       'North America', 'European Union', 'United States', 'Asia',
       'Lower middle income', 'South America', 'Russia', 'Brazil', 'India',
       'Germany', 'Italy', 'Africa', 'United Kingdom', 'France', 'Mexico',
       'Poland', 'South Korea', 'Spain', 'Turkey', 'Ukraine', 'Japan',
       'Vietnam', 'Canada', 'Indonesia', 'Argentina', 'Colombia', 'Peru',
       'Hong Kong', 'Iran', 'South Africa', 'Greece', 'Oceania', 'Philippines',
       'Thailand', 'Chile', 'Hungary', 'Australia', 'Bulgaria', 'Taiwan',
       'Romania'],
      dtype='object', name='Location')

DOWN 44 REGIONEN MIT EINER NIEDRIGEREN ZAHL VON WEEKLY DEATHS IM JAHR 2022:

Index(['Burundi', 'Djibouti', 'International', 'Macao', 'Tajikistan',
       'Wallis and Futuna', 'Cook Islands', 'Saint Pierre and Miquelon',
       'Benin', 'South Sudan', 'Sierra Leone', 'North Korea', 'Palau',
       'Anguilla', 'Gibraltar', 'Comoros', 'Liberia', 'Montserrat',
       'Equatorial Guinea', 'Turks and Caicos Islands', 'Tonga', 'Timor',
       'Vanuatu', 'Chad', 'Bonaire Sint Eustatius and Saba', 'Faeroe Islands',
       'Central African Republic', 'French Polynesia', 'Sao Tome and Principe',
       'Kiribati', 'Andorra', 'Congo', 'San Marino', 'Saint Kitts and Nevis',
       'Bhutan', 'Dominica', 'Liechtenstein', 'Cayman Islands', 'Gabon',
       'Guinea-Bissau', 'Monaco', 'Antigua and Barbuda', 'Greenland',
       'British Virgin Islands'],
      dtype='object', name='Location')


***** WEEKLY CASES *****

data_top enthält jetzt : 5612 lignes et 20 colonnes
data_down enthält jetzt : 5612 lignes et 20 colonnes 

TOP 44 REGIONEN MIT EINER HÖHEREN ZAHL VON WEEKLY CASES IM JAHR 2022:

Index(['World', 'High income', 'Europe', 'European Union', 'Asia',
       'Upper middle income', 'North America', 'United States',
       'Lower middle income', 'France', 'Germany', 'South America',
       'South Korea', 'Italy', 'Vietnam', 'Brazil', 'United Kingdom',
       'Oceania', 'India', 'Russia', 'Australia', 'Japan', 'Spain', 'Turkey',
       'Netherlands', 'Taiwan', 'Portugal', 'Argentina', 'Israel', 'Austria',
       'Denmark', 'Africa', 'Greece', 'Switzerland', 'Chile', 'Belgium',
       'Thailand', 'Poland', 'Mexico', 'Indonesia', 'Canada', 'Malaysia',
       'Czechia', 'Ukraine'],
      dtype='object', name='Location')

DOWN 44 REGIONEN MIT EINER NIEDRIGEREN ZAHL VON WEEKLY CASES IM JAHR 2022:

Index(['International', 'Wallis and Futuna', 'North Korea', 'Macao',
       'Tajikistan', 'Sierra Leone', 'Montserrat', 'Nicaragua', 'Liberia',
       'Yemen', 'Niger', 'Chad', 'Eritrea', 'Guinea-Bissau', 'Cook Islands',
       'Anguilla', 'Sao Tome and Principe', 'Benin', 'Djibouti', 'Gambia',
       'Saint Pierre and Miquelon', 'Comoros', 'South Sudan', 'Kiribati',
       'Central African Republic', 'Palau', 'Turks and Caicos Islands',
       'Timor', 'Saint Kitts and Nevis', 'Equatorial Guinea', 'Somalia',
       'Congo', 'British Virgin Islands', 'Burkina Faso',
       'Saint Vincent and the Grenadines', 'Antigua and Barbuda', 'Guinea',
       'Syria', 'Bonaire Sint Eustatius and Saba', 'Haiti', 'Lesotho', 'Togo',
       'Eswatini', 'Dominica'],
      dtype='object', name='Location')

#Anwendung des T-Tests für 2022
ttest1(list(data.drop(["Id", "Location"], axis=1).columns), ["Total Vaccinations", "Weekly Deaths", "Weekly Cases"],
       data_top_dict_2022, data_down_dict_2022, year=2022)


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN UNTERSCHIED IM MITTELWERT DER VARIABLEN ZWISCHEN DEN GRUPPEN MIT HOHEM UND NIEDRIGEM 'TOTAL VACCINATIONS' (2022) 

 
Weekly Cases---------------------------------------------------------- H0 GEWORFEN

Year------------------------------------------------------------------ 0
Weekly Cases per Million---------------------------------------------- H0 GEWORFEN

Weekly Deaths--------------------------------------------------------- H0 GEWORFEN

Weekly Deaths per Million--------------------------------------------- H0 GEWORFEN

Total Vaccinations---------------------------------------------------- H0 GEWORFEN

People Vaccinated----------------------------------------------------- 0
People Fully Vaccinated----------------------------------------------- 0
Total Boosters-------------------------------------------------------- 0
Daily Vaccinations---------------------------------------------------- H0 GEWORFEN

Total Vaccinations per Hundred---------------------------------------- H0 GEWORFEN

People Vaccinated per Hundred----------------------------------------- 0
People Fully Vaccinated per Hundred----------------------------------- 0
Total Boosters per Hundred-------------------------------------------- 0
Daily Vaccinations per Hundred---------------------------------------- H0 GEWORFEN

Daily People Vaccinated----------------------------------------------- 0
Daily People Vaccinated per Hundred----------------------------------- 0
Next Week's Deaths---------------------------------------------------- H0 GEWORFEN


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN UNTERSCHIED IM MITTELWERT DER VARIABLEN ZWISCHEN DEN GRUPPEN MIT HOHEM UND NIEDRIGEM 'WEEKLY DEATHS' (2022) 

 
Weekly Cases---------------------------------------------------------- H0 GEWORFEN

Year------------------------------------------------------------------ 0
Weekly Cases per Million---------------------------------------------- 0
Weekly Deaths--------------------------------------------------------- H0 GEWORFEN

Weekly Deaths per Million--------------------------------------------- 0
Total Vaccinations---------------------------------------------------- 0
People Vaccinated----------------------------------------------------- 0
People Fully Vaccinated----------------------------------------------- 0
Total Boosters-------------------------------------------------------- 0
Daily Vaccinations---------------------------------------------------- 0
Total Vaccinations per Hundred---------------------------------------- 0
People Vaccinated per Hundred----------------------------------------- 0
People Fully Vaccinated per Hundred----------------------------------- 0
Total Boosters per Hundred-------------------------------------------- 0
Daily Vaccinations per Hundred---------------------------------------- 0
Daily People Vaccinated----------------------------------------------- 0
Daily People Vaccinated per Hundred----------------------------------- 0
Next Week's Deaths---------------------------------------------------- H0 GEWORFEN


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN UNTERSCHIED IM MITTELWERT DER VARIABLEN ZWISCHEN DEN GRUPPEN MIT HOHEM UND NIEDRIGEM 'WEEKLY CASES' (2022) 

 
Weekly Cases---------------------------------------------------------- H0 GEWORFEN

Year------------------------------------------------------------------ 0
Weekly Cases per Million---------------------------------------------- 0
Weekly Deaths--------------------------------------------------------- H0 GEWORFEN

Weekly Deaths per Million--------------------------------------------- 0
Total Vaccinations---------------------------------------------------- 0
People Vaccinated----------------------------------------------------- 0
People Fully Vaccinated----------------------------------------------- 0
Total Boosters-------------------------------------------------------- 0
Daily Vaccinations---------------------------------------------------- 0
Total Vaccinations per Hundred---------------------------------------- 0
People Vaccinated per Hundred----------------------------------------- 0
People Fully Vaccinated per Hundred----------------------------------- 0
Total Boosters per Hundred-------------------------------------------- 0
Daily Vaccinations per Hundred---------------------------------------- 0
Daily People Vaccinated----------------------------------------------- 0
Daily People Vaccinated per Hundred----------------------------------- 0
Next Week's Deaths---------------------------------------------------- H0 GEWORFEN

C:\Users\kalil\anaconda3\Lib\site-packages\scipy\stats\_axis_nan_policy.py:523: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.
  res = hypotest_fun_out(*samples, **kwds)

#Anwendung des Spearmann-Tests für 2022,
from scipy.stats import spearmanr
spearmantest(list(data.drop(["Id", "Location"], axis=1).columns), ["Total Vaccinations", "Weekly Deaths", "Weekly Cases"],
       data_top_down_dict_2022, year="2022")


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN MONOTONEN ZUSAMMENHANG ZWISCHEN 'TOTAL VACCINATIONS' UND DEN ANDEREN VARIABLEN (2022) 

 
Total Vaccinations und Weekly Cases---------------------------------------------------------- H0 GEWORFEN (coef=0.603, p=0.000)

 Total Vaccinations und Year------------------------------------------------------------------ H0 BEHALTEN (coef=nan, p=nan)

Total Vaccinations und Weekly Cases per Million---------------------------------------------- H0 GEWORFEN (coef=-0.168, p=0.000)

Total Vaccinations und Weekly Deaths--------------------------------------------------------- H0 GEWORFEN (coef=0.648, p=0.000)

Total Vaccinations und Weekly Deaths per Million--------------------------------------------- H0 GEWORFEN (coef=-0.071, p=0.000)

 Total Vaccinations und People Vaccinated----------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und People Fully Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Total Boosters-------------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

Total Vaccinations und Daily Vaccinations---------------------------------------------------- H0 GEWORFEN (coef=0.828, p=0.000)

Total Vaccinations und Total Vaccinations per Hundred---------------------------------------- H0 GEWORFEN (coef=-0.156, p=0.000)

 Total Vaccinations und People Vaccinated per Hundred----------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und People Fully Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Total Boosters per Hundred-------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Daily Vaccinations per Hundred---------------------------------------- H0 BEHALTEN (coef=-0.020, p=0.130)

 Total Vaccinations und Daily People Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Daily People Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

Total Vaccinations und Next Week's Deaths---------------------------------------------------- H0 GEWORFEN (coef=0.640, p=0.000)


****************************************************************************************************


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN MONOTONEN ZUSAMMENHANG ZWISCHEN 'WEEKLY DEATHS' UND DEN ANDEREN VARIABLEN (2022) 

 
Weekly Deaths und Weekly Cases---------------------------------------------------------- H0 GEWORFEN (coef=0.922, p=0.000)

 Weekly Deaths und Year------------------------------------------------------------------ H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Weekly Cases per Million---------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Weekly Deaths per Million--------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Total Vaccinations---------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und People Vaccinated----------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und People Fully Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Total Boosters-------------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Daily Vaccinations---------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Total Vaccinations per Hundred---------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und People Vaccinated per Hundred----------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und People Fully Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Total Boosters per Hundred-------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Daily Vaccinations per Hundred---------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Daily People Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Daily People Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

Weekly Deaths und Next Week's Deaths---------------------------------------------------- H0 GEWORFEN (coef=0.953, p=0.000)


****************************************************************************************************


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN MONOTONEN ZUSAMMENHANG ZWISCHEN 'WEEKLY CASES' UND DEN ANDEREN VARIABLEN (2022) 

 
 Weekly Cases und Year------------------------------------------------------------------ H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Weekly Cases per Million---------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

Weekly Cases und Weekly Deaths--------------------------------------------------------- H0 GEWORFEN (coef=0.923, p=0.000)

 Weekly Cases und Weekly Deaths per Million--------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Total Vaccinations---------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und People Vaccinated----------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und People Fully Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Total Boosters-------------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Daily Vaccinations---------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Total Vaccinations per Hundred---------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und People Vaccinated per Hundred----------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und People Fully Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Total Boosters per Hundred-------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Daily Vaccinations per Hundred---------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Daily People Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Daily People Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

Weekly Cases und Next Week's Deaths---------------------------------------------------- H0 GEWORFEN (coef=0.923, p=0.000)


****************************************************************************************************

C:\Users\kalil\anaconda3\Lib\site-packages\scipy\stats\_stats_py.py:5445: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.
  warnings.warn(stats.ConstantInputWarning(warn_msg))

data_gereinigt = data.dropna(axis=0)
data_gereinigt.head()

#Anwendung des Spearmann-Tests für Die gesamten Daten,
from scipy.stats import spearmanr
spearmantest(list(data.drop(["Id", "Location"], axis=1).columns), ["Total Vaccinations", "Weekly Deaths", "Weekly Cases"],
       data_top_down_dict_2022)


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN MONOTONEN ZUSAMMENHANG ZWISCHEN 'TOTAL VACCINATIONS' UND DEN ANDEREN VARIABLEN (Gesamtdaten) 

 
Total Vaccinations und Weekly Cases---------------------------------------------------------- H0 GEWORFEN (coef=0.603, p=0.000)

 Total Vaccinations und Year------------------------------------------------------------------ H0 BEHALTEN (coef=nan, p=nan)

Total Vaccinations und Weekly Cases per Million---------------------------------------------- H0 GEWORFEN (coef=-0.168, p=0.000)

Total Vaccinations und Weekly Deaths--------------------------------------------------------- H0 GEWORFEN (coef=0.648, p=0.000)

Total Vaccinations und Weekly Deaths per Million--------------------------------------------- H0 GEWORFEN (coef=-0.071, p=0.000)

 Total Vaccinations und People Vaccinated----------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und People Fully Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Total Boosters-------------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

Total Vaccinations und Daily Vaccinations---------------------------------------------------- H0 GEWORFEN (coef=0.828, p=0.000)

Total Vaccinations und Total Vaccinations per Hundred---------------------------------------- H0 GEWORFEN (coef=-0.156, p=0.000)

 Total Vaccinations und People Vaccinated per Hundred----------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und People Fully Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Total Boosters per Hundred-------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Daily Vaccinations per Hundred---------------------------------------- H0 BEHALTEN (coef=-0.020, p=0.130)

 Total Vaccinations und Daily People Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Total Vaccinations und Daily People Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

Total Vaccinations und Next Week's Deaths---------------------------------------------------- H0 GEWORFEN (coef=0.640, p=0.000)


****************************************************************************************************


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN MONOTONEN ZUSAMMENHANG ZWISCHEN 'WEEKLY DEATHS' UND DEN ANDEREN VARIABLEN (Gesamtdaten) 

 
Weekly Deaths und Weekly Cases---------------------------------------------------------- H0 GEWORFEN (coef=0.922, p=0.000)

 Weekly Deaths und Year------------------------------------------------------------------ H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Weekly Cases per Million---------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Weekly Deaths per Million--------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Total Vaccinations---------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und People Vaccinated----------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und People Fully Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Total Boosters-------------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Daily Vaccinations---------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Total Vaccinations per Hundred---------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und People Vaccinated per Hundred----------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und People Fully Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Total Boosters per Hundred-------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Daily Vaccinations per Hundred---------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Daily People Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Deaths und Daily People Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

Weekly Deaths und Next Week's Deaths---------------------------------------------------- H0 GEWORFEN (coef=0.953, p=0.000)


****************************************************************************************************


 HYPOTHESE H0 : ES GIBT KEINEN SIGNIFIKANTEN MONOTONEN ZUSAMMENHANG ZWISCHEN 'WEEKLY CASES' UND DEN ANDEREN VARIABLEN (Gesamtdaten) 

 
 Weekly Cases und Year------------------------------------------------------------------ H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Weekly Cases per Million---------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

Weekly Cases und Weekly Deaths--------------------------------------------------------- H0 GEWORFEN (coef=0.923, p=0.000)

 Weekly Cases und Weekly Deaths per Million--------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Total Vaccinations---------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und People Vaccinated----------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und People Fully Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Total Boosters-------------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Daily Vaccinations---------------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Total Vaccinations per Hundred---------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und People Vaccinated per Hundred----------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und People Fully Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Total Boosters per Hundred-------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Daily Vaccinations per Hundred---------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Daily People Vaccinated----------------------------------------------- H0 BEHALTEN (coef=nan, p=nan)

 Weekly Cases und Daily People Vaccinated per Hundred----------------------------------- H0 BEHALTEN (coef=nan, p=nan)

Weekly Cases und Next Week's Deaths---------------------------------------------------- H0 GEWORFEN (coef=0.923, p=0.000)


****************************************************************************************************

C:\Users\kalil\anaconda3\Lib\site-packages\scipy\stats\_stats_py.py:5445: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.
  warnings.warn(stats.ConstantInputWarning(warn_msg))

from sklearn.model_selection import train_test_split

dataf = data.copy()

dataf.head(5)

train_set, val_set = train_test_split(dataf, test_size=0.3, random_state=0 )

print(f"Die Grösse von Train_set : {train_set.shape}")
print(f"Die Grösse von Val_set : {val_set.shape}")

Die Grösse von Train_set : (90409, 20)
Die Grösse von Val_set : (38747, 20)

for col in dataf.select_dtypes('object'):
    print(f'{col :-<50} \n\n{dataf[col].unique()}')

Location------------------------------------------ 

['World' 'Afghanistan' 'Africa' 'Albania' 'Algeria' 'Andorra' 'Angola'
 'Anguilla' 'Antigua and Barbuda' 'Argentina' 'Armenia' 'Aruba' 'Asia'
 'Australia' 'Austria' 'Azerbaijan' 'Bahamas' 'Bahrain' 'Bangladesh'
 'Barbados' 'Belarus' 'Belgium' 'Belize' 'Benin' 'Bermuda' 'Bhutan'
 'Bolivia' 'Bonaire Sint Eustatius and Saba' 'Bosnia and Herzegovina'
 'Botswana' 'Brazil' 'British Virgin Islands' 'Brunei' 'Bulgaria'
 'Burkina Faso' 'Burundi' 'Cambodia' 'Cameroon' 'Canada' 'Cape Verde'
 'Cayman Islands' 'Central African Republic' 'Chad' 'Chile' 'China'
 'Colombia' 'Comoros' 'Congo' 'Cook Islands' 'Costa Rica' "Cote d'Ivoire"
 'Croatia' 'Cuba' 'Curacao' 'Cyprus' 'Czechia'
 'Democratic Republic of Congo' 'Denmark' 'Djibouti' 'Dominica'
 'Dominican Republic' 'Ecuador' 'Egypt' 'El Salvador' 'Equatorial Guinea'
 'Eritrea' 'Estonia' 'Eswatini' 'Ethiopia' 'Europe' 'European Union'
 'Faeroe Islands' 'Fiji' 'Finland' 'France' 'French Polynesia' 'Gabon'
 'Gambia' 'Georgia' 'Germany' 'Ghana' 'Gibraltar' 'Greece' 'Greenland'
 'Grenada' 'Guatemala' 'Guinea' 'Guinea-Bissau' 'Guyana' 'Haiti'
 'High income' 'Honduras' 'Hong Kong' 'Hungary' 'Iceland' 'India'
 'Indonesia' 'International' 'Iran' 'Iraq' 'Ireland' 'Isle of Man'
 'Israel' 'Italy' 'Jamaica' 'Japan' 'Jordan' 'Kazakhstan' 'Kenya'
 'Kiribati' 'Kosovo' 'Kuwait' 'Kyrgyzstan' 'Laos' 'Latvia' 'Lebanon'
 'Lesotho' 'Liberia' 'Libya' 'Liechtenstein' 'Lithuania' 'Low income'
 'Lower middle income' 'Luxembourg' 'Macao' 'Madagascar' 'Malawi'
 'Malaysia' 'Maldives' 'Mali' 'Malta' 'Mauritania' 'Mauritius' 'Mexico'
 'Moldova' 'Monaco' 'Mongolia' 'Montenegro' 'Montserrat' 'Morocco'
 'Mozambique' 'Myanmar' 'Namibia' 'Nepal' 'Netherlands' 'New Caledonia'
 'New Zealand' 'Nicaragua' 'Niger' 'Nigeria' 'North America' 'North Korea'
 'North Macedonia' 'Norway' 'Oceania' 'Oman' 'Pakistan' 'Palau'
 'Palestine' 'Panama' 'Papua New Guinea' 'Paraguay' 'Peru' 'Philippines'
 'Poland' 'Portugal' 'Qatar' 'Romania' 'Russia' 'Rwanda'
 'Saint Kitts and Nevis' 'Saint Lucia' 'Saint Pierre and Miquelon'
 'Saint Vincent and the Grenadines' 'Samoa' 'San Marino'
 'Sao Tome and Principe' 'Saudi Arabia' 'Senegal' 'Serbia' 'Seychelles'
 'Sierra Leone' 'Singapore' 'Slovakia' 'Slovenia' 'Solomon Islands'
 'Somalia' 'South Africa' 'South America' 'South Korea' 'South Sudan'
 'Spain' 'Sri Lanka' 'Sudan' 'Suriname' 'Sweden' 'Switzerland' 'Syria'
 'Taiwan' 'Tajikistan' 'Tanzania' 'Thailand' 'Timor' 'Togo' 'Tonga'
 'Trinidad and Tobago' 'Tunisia' 'Turkey' 'Turks and Caicos Islands'
 'Uganda' 'Ukraine' 'United Arab Emirates' 'United Kingdom'
 'United States' 'Upper middle income' 'Uruguay' 'Uzbekistan' 'Vanuatu'
 'Venezuela' 'Vietnam' 'Wallis and Futuna' 'Yemen' 'Zambia' 'Zimbabwe']

cols_per_row = 2
num_cols = len([col for col in train_set.drop(["Location", "Id"], axis=1).columns] )
rows = (num_cols + cols_per_row - 1) // cols_per_row  #Berechnung der Anzahl von erforderlichen Zeilen

fig, axes = plt.subplots(rows, cols_per_row, figsize=(12, 5 * rows))
axes = axes.flatten() 

for i, col in enumerate(train_set.drop(["Location", "Id"], axis=1).columns):
    sns.histplot(ax = axes[i], x = train_set[col], bins=50, kde=True,  color='chocolate')
    axes[i].set_title(f'{col}', fontsize=14)

#Unnötige Achsen ausblenden, wenn die Spaltenanzahl ungerade ist
for j in range(num_cols, len(axes)):
    axes[j].set_visible(False)

#plt.tight_layout()
plt.show()

from sklearn.feature_selection import SelectKBest, f_regression
from sklearn.preprocessing import OrdinalEncoder
import numpy as np
from sklearn.preprocessing import RobustScaler
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
from sklearn.model_selection import learning_curve
from math import sqrt


class Preprocessing:
    
    def __init__(self, df):
        self.df = df

    #Definition der Encoder Funktion
    def encoding(self) : 
        ord_encoder = OrdinalEncoder()
        for col in self.df.select_dtypes('object'):
            self.df[col] = ord_encoder.fit_transform(self.df[[col]])
    
       

    #Definition der Funktion Imputation
    def imputation(self):
        self.df = self.df.dropna(axis=0).reset_index(drop=True)
        #print(self.df.head(10))

    
    #Definition der Funktion Logarithme 
    #Der Logarithmus reduziert die Asymmetrien und stabilisiert die Varianz
    def logarithme(self):
        for col in list(self.df.columns):
            #if self.df[col].skew() >= 1:
            self.df.loc[:, col] =  np.log1p(self.df[col]) # = np.log(df[col]+1)
    
        
    

    #Definition der Funktion feature_engineering
    def feature_engineering(self):
    
        self.df = self.df.copy()
        
        self.df['Change in Weekly Cases'] = self.df['Weekly Cases'] - self.df['Weekly Cases'].shift(1)
        self.df['Change in Weekly Deaths'] = self.df['Weekly Deaths'] - self.df['Weekly Deaths'].shift(1)
    
        # Die ersten beiden Zeilen der beiden Spalten mit dem Wert 0 füllen.
        self.df['Change in Weekly Cases'].fillna(0, inplace=True)
        self.df['Change in Weekly Deaths'].fillna(0, inplace=True)



    #Definition der Funktion RobustScaler
    def robust_scaler(self, df, target):
        
        df_r_scaler = RobustScaler()
        target_r_scaler = RobustScaler()
        
        df_scaled =  df_r_scaler.fit_transform(df)
        target_scaled = target_r_scaler.fit_transform(target.to_numpy().reshape(-1, 1)) #weil robust_scaler akzeptiert nur ein 2D array und ein Pandas serie hat keine Methode Reshape

        df_scaled = pd.DataFrame(df_scaled, columns=df.columns) #In ein DataFrame mit den gleichen Spalten umwandeln, weil R_scaler gibt ein numy array zurück
        target_scaled = pd.DataFrame(target_scaled, columns=[target.name])
        
        return df_scaled, target_scaled, target_r_scaler
    
    
    

    #Feature Selection
    #Wichtig : score_func=f_regression ist für eine kontinuierlische numerische Zielvariable
    def feature_selector(self, df, target, k):
        target1 = target.to_numpy().ravel() #weil der selektor ein 1D array braucht
        selector = SelectKBest(score_func=f_regression, k=k)
        selector.fit_transform(df, target1)
        selected_features = df.columns[selector.get_support()]
        
        return df[list(selected_features)]


    # Definition der Preprocessing Funktion
    def process(self):
        self.encoding()
        self.imputation()
        self.logarithme()
        self.feature_engineering()
        
        X = self.df.drop("Next Week's Deaths", axis=1)
        y = self.df["Next Week's Deaths"]

        X, y, val_r_scaler = self.robust_scaler(X, y)
        
        #X = self.feature_selector(X, y, 20)
 
        return X, y, val_r_scaler



    #Bewertungsfunktion
    def evaluation(self, model, X_train, y_train, X_val, y_val, val_r_scaler):

        y_train = y_train.to_numpy().ravel()


        #Learning_curve and cross-validation
        N, train_score, val_score = learning_curve( model, X_train, y_train,
                                                   cv = 5, scoring='r2', train_sizes=np.linspace(0.1, 1, 10))

        #training des Models
        model.fit(X_train, y_train)
        y_pred = model.predict(X_val)

        # print(y_pred.shape)
        # print(y_val.shape)

        # print(y_pred)
        # print(y_val)
    
        # Berechnung der Metriken, Anwendung des Inversen des Logarithmus und RobustScaler Vorher, um zu den ursprünglichen Daten zurückzukehren
        y_pred = np.expm1(val_r_scaler.inverse_transform(y_pred.reshape(-1,1)))  # RobustScaler erwartet ein 2D array, y_pred ist 1D
        y_val = np.expm1(val_r_scaler.inverse_transform(y_val.to_numpy())) # to_numpy, weil y_val ein pandas dataframe ist

        # print(y_pred)
        # print(y_val)
        
        mae = mean_absolute_error(y_pred, y_val)
        mse = mean_squared_error(y_pred, y_val)
        rmse = sqrt(mse)
        r2s = r2_score(y_pred, y_val)
        
        #Erstellung eines Dataframes der Metriken
        RegMetriken = pd.DataFrame(
            {
                "EVALUATION METRICS": ["Mean Absolute Erroe", "Mean Squared Error", "R2 Score", "Root Mean Squared Error"],
                "SCORES": [mae, mse, r2s, rmse]
            }
        )
        
        print(f"\n\nEvaluationsergebnisse des Models\n\n {RegMetriken}\n\n")
        
        
    
        plt.figure(figsize=(14,7))
        
        plt.subplot(1,2,1)
        plt.plot(N, train_score.mean(axis=1), label='Train Score mit cv=5')
        plt.plot(N, val_score.mean(axis=1), label='Validation Score mit cv=5')
        plt.legend()
    
        plt.subplot(1,2,2)
        plt.scatter(y_val, y_pred)
        plt.xlabel("y Validation")
        plt.ylabel("y Predicted")
    
        plt.tight_layout()
        plt.show()
    
        #Vergleich der reellen Werte mit den vorhersagten Werten
        comparison = pd.DataFrame({
        "Reelle Werte(y_val)" : y_val.ravel() if len(y_val.shape) > 1 else y_val, #ravel, Weil pd.Dataframe braucht ein 1D array, Flatten with ravel()
        "Vorhersagte Werte(y_pred)" : y_pred.ravel() if len(y_pred.shape) > 1 else y_pred
    })
        comparison['Absolute Error'] = abs(comparison["Reelle Werte(y_val)"] - comparison["Vorhersagte Werte(y_pred)"])
    
        print(f"Vergleich von den vohersagten Werten zu den reellen Werten\n\n{comparison.head(10)}")
        
    
        print("\n****************************************************************************************************************************************")

#Aufruf von preprocessing funktion
train_preprocessing = Preprocessing(train_set)
X_train, y_train, _ = train_preprocessing.process()

val_preprocessing = Preprocessing(val_set)
X_val, y_val, val_r_scaler = val_preprocessing.process()

X_train.head()

y_train.head()

X_val.head()

y_val.head()

cols_per_row = 2

# Vérification de la présence des colonnes avant de les supprimer
columns_to_drop = ["Location", "Id"] if all(col in X_train.columns for col in ["Location", "Id"]) else []
filtered_X_train = X_train.drop(columns_to_drop, axis=1)

num_cols = len(filtered_X_train.columns)
rows = (num_cols + cols_per_row - 1) // cols_per_row  #Berechnung der Anzahl von erforderlichen Zeilen

fig, axes = plt.subplots(rows, cols_per_row, figsize=(12, 5 * rows))
axes = axes.flatten() 

for i, col in enumerate(filtered_X_train.columns):
    sns.histplot(ax = axes[i], x = X_train[col], bins=50, kde=True,  color='chocolate')
    axes[i].set_title(f'{col}', fontsize=14)


#Unnötige Achsen ausblenden, wenn die Spaltenanzahl ungerade ist
for j in range(num_cols, len(axes)):
    axes[j].set_visible(False)

plt.tight_layout()
plt.show()

#Verteilung des Targets nach preprocessing
sns.histplot(y_train, bins=50, kde=True,  color='chocolate')
plt.title(f'{col}', fontsize=14)

Text(0.5, 1.0, 'Change in Weekly Deaths')

from sklearn.linear_model import LinearRegression

model = LinearRegression()
val_preprocessing.evaluation(model, X_train, y_train, X_val, y_val, val_r_scaler)


Evaluationsergebnisse des Models

         EVALUATION METRICS        SCORES
0      Mean Absolute Erroe  4.151796e+02
1       Mean Squared Error  1.768300e+06
2                 R2 Score  9.855552e-01
3  Root Mean Squared Error  1.329775e+03

Vergleich von den vohersagten Werten zu den reellen Werten

   Reelle Werte(y_val)  Vorhersagte Werte(y_pred)  Absolute Error
0              37695.0               35331.979293     2363.020707
1                 74.0                  35.550778       38.449222
2                 17.0                   8.915562        8.084438
3              28560.0               25497.210696     3062.789304
4                  5.0                  17.077282       12.077282
5                  0.0                  -0.150388        0.150388
6                  5.0                   3.613460        1.386540
7                  3.0                   3.277905        0.277905
8               8548.0                9534.149272      986.149272
9                 46.0                  42.145341        3.854659

****************************************************************************************************************************************

from sklearn.model_selection import GridSearchCV
import time
from tqdm import tqdm

class Training(Preprocessing):

    def __init__(self, df):
        super().__init__(df)
        
    #Redefinition der Methode process() 
    def process(self):
        self.encoding()
        self.imputation()
        self.logarithme()
        self.feature_engineering()
        
        X = self.df.drop("Next Week's Deaths", axis=1)
        y = self.df["Next Week's Deaths"]

        X, y, val_r_scaler = self.robust_scaler(X, y)
        
        return X, y, val_r_scaler



    #Bewertungsfunktion
    def assess(self, model, X_train, y_train, X_val, y_val, val_r_scaler):

        y_train = y_train.to_numpy().ravel()

        #training des Models
        model.fit(X_train, y_train)
        y_pred = model.predict(X_val)

        # Berechnung der Metriken, Anwendung des Inversen des Logarithmus und RobustScaler Vorher, um zu den ursprünglichen Daten zurückzukehren
        y_pred = np.expm1(val_r_scaler.inverse_transform(y_pred.reshape(-1,1))) 
        y_val = np.expm1(val_r_scaler.inverse_transform(y_val.to_numpy())) 

        mae = mean_absolute_error(y_pred, y_val)
        mse = mean_squared_error(y_pred, y_val)
        rmse = sqrt(mse)
        r2s = r2_score(y_pred, y_val)
        
        #Erstellung eines Dataframes der Metriken
        RegMetriken = pd.DataFrame(
            {
                "EVALUATION METRICS": ["Mean Absolute Erroe", "Mean Squared Error", "R2 Score", "Root Mean Squared Error"],
                "SCORES": [mae, mse, r2s, rmse]
            }
        )
        
        print(f"\n\nEvaluationsergebnisse des Models\n\n {RegMetriken}\n\n")
        
        plt.figure()
        
        plt.scatter(y_val, y_pred)
        plt.xlabel("y Validation")
        plt.ylabel("y Predicted")
    
        plt.show()
    
        #Vergleich der reellen Werte mit den vorhersagten Werten
        comparison = pd.DataFrame({
        "Reelle Werte(y_val)" : y_val.ravel() if len(y_val.shape) > 1 else y_val, #ravel, Weil pd.Dataframe braucht ein 1D array, Flatten with ravel()
        "Vorhersagte Werte(y_pred)" : y_pred.ravel() if len(y_pred.shape) > 1 else y_pred
    })
        comparison['Absolute Error'] = abs(comparison["Reelle Werte(y_val)"] - comparison["Vorhersagte Werte(y_pred)"])
    
        print(f"Vergleich von den vohersagten Werten zu den reellen Werten\n\n{comparison.head(10)}")
        


    def optimize(self, model_dict, param_dict, X_train, y_train, X_val, y_val, val_r_scaler):
            start_time = time.time()
            print(f"Optimisation started at {time.strftime('%H:%M:%S', time.localtime(start_time))}")
        
            for name, model in tqdm(model_dict.items(), desc="Optimisation in process", unit="model"):
                print(f"************************* {name.upper()} ****************************")
                grid = GridSearchCV(model, param_dict[name], cv = 5, scoring='r2')
                self.assess(grid, X_train, y_train, X_val, y_val, val_r_scaler)
                print(f"BEST ESTIMATOR : {grid.best_estimator_.get_params()}")
                print(f"BEST PARAMETERS : {grid.best_params_}")
                print(f"BEST SCORE : {grid.best_score_}")
                
                print("\n****************************************************************************************************************************************")

            # Anzeige der Ausführungszeit
            end_time = time.time()
            elapsed_time = end_time - start_time
            estimated_end_time = start_time + elapsed_time  # Heure estimée de fin
            print(f"Optimisation finished at {time.strftime('%H:%M:%S', time.localtime(estimated_end_time))}")
            print(f"Total time : {elapsed_time / 60:.2f} minutes")

from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR

KNN = KNeighborsRegressor()
RandomForest = RandomForestRegressor(random_state=0)
SVR = SVR()
DecisionTree = DecisionTreeRegressor(random_state=0)
LRegressor = LinearRegression()

model_dict = {
    'KNN': KNN,
    'SVR': SVR,
    'DecisionTree': DecisionTree,
    'LRegressor': LRegressor,
    'RandomForest': RandomForest
}

train_training = Training(train_set)
X_train, y_train, _ = train_training.process()

val_Training = Training(val_set)
X_val, y_val, val_r_scaler = val_Training.process()

X_val.head()

y_val.head()

import time
from tqdm import tqdm

start_time = time.time()
print(f"Training started at {time.strftime('%H:%M:%S', time.localtime(start_time))}")

for name, model in tqdm(model_dict.items(), desc="Training in process", unit="model"):
    print(f"************************* {name.upper()} ****************************")
    train_training.evaluation(model, X_train, y_train, X_val, y_val, val_r_scaler)

# Anzeige der Ausführungszeit
end_time = time.time()
elapsed_time = end_time - start_time
estimated_end_time = start_time + elapsed_time  # Heure estimée de fin
print(f"Training finished at {time.strftime('%H:%M:%S', time.localtime(estimated_end_time))}")
print(f"Total time : {elapsed_time / 60:.2f} minutes")

Training started at 14:58:43

Training in process:   0%|          | 0/5 [00:00<?, ?model/s]

************************* KNN ****************************


Evaluationsergebnisse des Models

         EVALUATION METRICS        SCORES
0      Mean Absolute Erroe  5.439604e+02
1       Mean Squared Error  3.409667e+06
2                 R2 Score  9.732547e-01
3  Root Mean Squared Error  1.846528e+03

Training in process:  20%|██        | 1/5 [00:10<00:40, 10.13s/model]

Vergleich von den vohersagten Werten zu den reellen Werten

   Reelle Werte(y_val)  Vorhersagte Werte(y_pred)  Absolute Error
0              37695.0               19445.207310    18249.792690
1                 74.0                  42.697860       31.302140
2                 17.0                  11.593919        5.406081
3              28560.0               30775.108425     2215.108425
4                  5.0                   8.819630        3.819630
5                  0.0                  -0.124869        0.124869
6                  5.0                   4.798278        0.201722
7                  3.0                   4.880070        1.880070
8               8548.0                9679.340430     1131.340430
9                 46.0                  94.154328       48.154328

****************************************************************************************************************************************
************************* SVR ****************************


Evaluationsergebnisse des Models

         EVALUATION METRICS        SCORES
0      Mean Absolute Erroe  7.829830e+02
1       Mean Squared Error  8.060651e+06
2                 R2 Score  9.223819e-01
3  Root Mean Squared Error  2.839129e+03

Training in process:  40%|████      | 2/5 [00:57<01:35, 31.98s/model]

Vergleich von den vohersagten Werten zu den reellen Werten

   Reelle Werte(y_val)  Vorhersagte Werte(y_pred)  Absolute Error
0              37695.0               23656.488879    14038.511121
1                 74.0                  37.888822       36.111178
2                 17.0                   8.969101        8.030899
3              28560.0               34637.043302     6077.043302
4                  5.0                  15.811865       10.811865
5                  0.0                  -0.107570        0.107570
6                  5.0                   4.459964        0.540036
7                  3.0                   4.209750        1.209750
8               8548.0                8992.794064      444.794064
9                 46.0                  66.684992       20.684992

****************************************************************************************************************************************
************************* DECISIONTREE ****************************


Evaluationsergebnisse des Models

         EVALUATION METRICS        SCORES
0      Mean Absolute Erroe  3.521795e+02
1       Mean Squared Error  1.526965e+06
2                 R2 Score  9.886686e-01
3  Root Mean Squared Error  1.235704e+03

Training in process:  60%|██████    | 3/5 [01:01<00:38, 19.17s/model]

Vergleich von den vohersagten Werten zu den reellen Werten

   Reelle Werte(y_val)  Vorhersagte Werte(y_pred)  Absolute Error
0              37695.0               38450.765753      755.765753
1                 74.0                  13.602752       60.397248
2                 17.0                  14.529680        2.470320
3              28560.0               31941.457392     3381.457392
4                  5.0                  10.827472        5.827472
5                  0.0                  -0.124869        0.124869
6                  5.0                   0.768737        4.231263
7                  3.0                   2.574817        0.425183
8               8548.0                9589.987510     1041.987510
9                 46.0                  31.324574       14.675426

****************************************************************************************************************************************
************************* LREGRESSOR ****************************


Evaluationsergebnisse des Models

         EVALUATION METRICS        SCORES
0      Mean Absolute Erroe  4.151796e+02
1       Mean Squared Error  1.768300e+06
2                 R2 Score  9.855552e-01
3  Root Mean Squared Error  1.329775e+03

Training in process:  80%|████████  | 4/5 [01:01<00:11, 11.76s/model]

for name, model in model_dict.items():
    print(f"******************{name.upper()}*******************")
    print(model.get_params())

******************KNN*******************
{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': None, 'n_neighbors': 5, 'p': 2, 'weights': 'uniform'}
******************SVR*******************
{'C': 1.0, 'cache_size': 200, 'coef0': 0.0, 'degree': 3, 'epsilon': 0.1, 'gamma': 'scale', 'kernel': 'rbf', 'max_iter': -1, 'shrinking': True, 'tol': 0.001, 'verbose': False}
******************DECISIONTREE*******************
{'ccp_alpha': 0.0, 'criterion': 'squared_error', 'max_depth': None, 'max_features': None, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': 0, 'splitter': 'best'}
******************LREGRESSOR*******************
{'copy_X': True, 'fit_intercept': True, 'n_jobs': None, 'positive': False}
******************RANDOMFOREST*******************
{'bootstrap': True, 'ccp_alpha': 0.0, 'criterion': 'squared_error', 'max_depth': None, 'max_features': 1.0, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 0, 'verbose': 0, 'warm_start': False}

knn_hyper_params = {
    'n_neighbors': [3,5, 7, 10],
    'weights': ['uniform', 'distance'],
    'p': [1, 2],
}

svr_hyper_params = {
    'C': [0.1, 1.0],
    
}

tree_hyper_params = {
    'max_depth': [5, 10, 15, 20, None],
    
}
lregressor_hyper_params = {
    'positive': [False, True]
}

randomforest_hyper_params = {
    'max_depth': [5, 10, None],
    'min_samples_leaf': [1, 2],
    'min_samples_split': [2, 3],
    'n_estimators': [100]
    
}

param_dict = {
    'KNN': knn_hyper_params,
    'SVR': svr_hyper_params,
    'DecisionTree': tree_hyper_params,
    'LRegressor': lregressor_hyper_params,
    'RandomForest': randomforest_hyper_params
}

train_training.optimize(model_dict, param_dict, X_train, y_train, X_val, y_val, val_r_scaler)

Optimisation started at 15:06:56

Optimisation in process:   0%|          | 0/5 [00:00<?, ?model/s]

************************* KNN ****************************


Evaluationsergebnisse des Models

         EVALUATION METRICS        SCORES
0      Mean Absolute Erroe  3.235717e+02
1       Mean Squared Error  1.202035e+06
2                 R2 Score  9.908134e-01
3  Root Mean Squared Error  1.096373e+03

Optimisation in process:  20%|██        | 1/5 [00:13<00:55, 13.99s/model]

Vergleich von den vohersagten Werten zu den reellen Werten

   Reelle Werte(y_val)  Vorhersagte Werte(y_pred)  Absolute Error
0              37695.0               24002.241892    13692.758108
1                 74.0                  50.192312       23.807688
2                 17.0                  11.505044        5.494956
3              28560.0               31444.161664     2884.161664
4                  5.0                   8.471164        3.471164
5                  0.0                  -0.124869        0.124869
6                  5.0                   4.398123        0.601877
7                  3.0                   4.481572        1.481572
8               8548.0                8394.523774      153.476226
9                 46.0                  59.712906       13.712906
BEST ESTIMATOR : {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': None, 'n_neighbors': 5, 'p': 1, 'weights': 'distance'}
BEST PARAMETERS : {'n_neighbors': 5, 'p': 1, 'weights': 'distance'}
BEST SCORE : 0.9857486645527

****************************************************************************************************************************************
************************* SVR ****************************


Evaluationsergebnisse des Models

         EVALUATION METRICS        SCORES
0      Mean Absolute Erroe  7.829830e+02
1       Mean Squared Error  8.060651e+06
2                 R2 Score  9.223819e-01
3  Root Mean Squared Error  2.839129e+03

Optimisation in process:  40%|████      | 2/5 [00:25<00:37, 12.58s/model]

Vergleich von den vohersagten Werten zu den reellen Werten

   Reelle Werte(y_val)  Vorhersagte Werte(y_pred)  Absolute Error
0              37695.0               23656.488879    14038.511121
1                 74.0                  37.888822       36.111178
2                 17.0                   8.969101        8.030899
3              28560.0               34637.043302     6077.043302
4                  5.0                  15.811865       10.811865
5                  0.0                  -0.107570        0.107570
6                  5.0                   4.459964        0.540036
7                  3.0                   4.209750        1.209750
8               8548.0                8992.794064      444.794064
9                 46.0                  66.684992       20.684992
BEST ESTIMATOR : {'C': 1.0, 'cache_size': 200, 'coef0': 0.0, 'degree': 3, 'epsilon': 0.1, 'gamma': 'scale', 'kernel': 'rbf', 'max_iter': -1, 'shrinking': True, 'tol': 0.001, 'verbose': False}
BEST PARAMETERS : {'C': 1.0}
BEST SCORE : 0.9786340994259801

****************************************************************************************************************************************
************************* DECISIONTREE ****************************


Evaluationsergebnisse des Models

         EVALUATION METRICS        SCORES
0      Mean Absolute Erroe  3.759650e+02
1       Mean Squared Error  1.385560e+06
2                 R2 Score  9.895087e-01
3  Root Mean Squared Error  1.177098e+03

Optimisation in process:  60%|██████    | 3/5 [00:28<00:16,  8.19s/model]

Vergleich von den vohersagten Werten zu den reellen Werten

   Reelle Werte(y_val)  Vorhersagte Werte(y_pred)  Absolute Error
0              37695.0               36137.545756     1557.454244
1                 74.0                  38.431932       35.568068
2                 17.0                   4.395264       12.604736
3              28560.0               32619.217244     4059.217244
4                  5.0                  14.474709        9.474709
5                  0.0                  -0.061780        0.061780
6                  5.0                   6.116067        1.116067
7                  3.0                   2.574817        0.425183
8               8548.0                9150.612582      602.612582
9                 46.0                  28.012211       17.987789
BEST ESTIMATOR : {'ccp_alpha': 0.0, 'criterion': 'squared_error', 'max_depth': 10, 'max_features': None, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': 0, 'splitter': 'best'}
BEST PARAMETERS : {'max_depth': 10}
BEST SCORE : 0.9776904900227367

****************************************************************************************************************************************
************************* LREGRESSOR ****************************


Evaluationsergebnisse des Models

         EVALUATION METRICS        SCORES
0      Mean Absolute Erroe  4.151796e+02
1       Mean Squared Error  1.768300e+06
2                 R2 Score  9.855552e-01
3  Root Mean Squared Error  1.329775e+03

Optimisation in process:  80%|████████  | 4/5 [00:28<00:05,  5.01s/model]

from sklearn.ensemble import VotingRegressor

#Anwendung von schon trainierten Modellen
voting_model = VotingRegressor([(name, model) for name,model in model_dict.items()])

train_training.assess(voting_model, X_train, y_train, X_val, y_val, val_r_scaler)


Evaluationsergebnisse des Models

         EVALUATION METRICS        SCORES
0      Mean Absolute Erroe  3.178563e+02
1       Mean Squared Error  1.021972e+06
2                 R2 Score  9.915009e-01
3  Root Mean Squared Error  1.010926e+03

Vergleich von den vohersagten Werten zu den reellen Werten

   Reelle Werte(y_val)  Vorhersagte Werte(y_pred)  Absolute Error
0              37695.0               29852.001794     7842.998206
1                 74.0                  32.844500       41.155500
2                 17.0                  10.751734        6.248266
3              28560.0               30700.664524     2140.664524
4                  5.0                  12.783765        7.783765
5                  0.0                  -0.116733        0.116733
6                  5.0                   3.370241        1.629759
7                  3.0                   3.397921        0.397921
8               8548.0                9473.739624      925.739624
9                 46.0                  50.711213        4.711213

from sklearn.ensemble import BaggingRegressor

bagging_model = BaggingRegressor(estimator = RandomForestRegressor(n_estimators=100, random_state=0), 
                                 n_estimators = 10, random_state=0)

train_training.assess(bagging_model, X_train, y_train, X_val, y_val, val_r_scaler)


Evaluationsergebnisse des Models

         EVALUATION METRICS        SCORES
0      Mean Absolute Erroe  3.082179e+02
1       Mean Squared Error  1.022234e+06
2                 R2 Score  9.923474e-01
3  Root Mean Squared Error  1.011056e+03

Vergleich von den vohersagten Werten zu den reellen Werten

   Reelle Werte(y_val)  Vorhersagte Werte(y_pred)  Absolute Error
0              37695.0               37328.653960      366.346040
1                 74.0                  46.933021       27.066979
2                 17.0                   8.652566        8.347434
3              28560.0               31405.944083     2845.944083
4                  5.0                  13.697607        8.697607
5                  0.0                  -0.024513        0.024513
6                  5.0                   5.294709        0.294709
7                  3.0                   3.060650        0.060650
8               8548.0                9606.377374     1058.377374
9                 46.0                  38.737063        7.262937

import joblib

joblib.dump(model_dict['RandomForest'], 'RandomForest.pkl')
joblib.dump(bagging_model, 'bagging_model.pkl')
joblib.dump(voting_model, 'voting_model.pkl')
joblib.dump(model_dict['KNN'], 'KNN_regressor.pkl')

['KNN_regressor.pkl']

class Test(Training):

    def __init__(self, df):
        super().__init__(df)

    #Redefinition der Methode assess() (wichtig: die Klasse Test erbt von der Klasse Training und assess() ist eine Methode der Klasse Training)
    def assess(self, model, X_test, test_df_r_scaler):
        #test
        y_pred = model.predict(X_test)
        
        # Berechnung der Metriken, Anwendung des Inversen des Logarithmus und RobustScaler Vorher, um zu den ursprünglichen Daten zurückzukehren
        y_pred = np.expm1(val_r_scaler.inverse_transform(y_pred.reshape(-1,1))) 

        print(y_pred)

        return y_pred

    

    


    def test(self, test_model_dict, X_test, test_df_r_scaler):

        list_prediction = []

        for name, model in tqdm(test_model_dict.items(), desc="Test in process", unit="model"):
            
                print(f"************************* {name.upper()} ****************************")
                list_prediction.append(self.assess(model, X_test, test_df_r_scaler))
                
                print("\n****************************************************************************************************************************************")
        return list_prediction


    #Redefinition der Methode process() (wichtig: die Klasse Test erbt von der Klasse Training, die wiederum von der Klasse Preprocessing erbt, und process() ist eine Methode der Klasse Preprocessing) 
    def process(self):
        self.encoding()
        self.imputation()
        
        ids = self.df['Id'] # wir brauchen Ids am Ende
        
        self.logarithme()
        self.feature_engineering()
        
        X = self.df

        X, test_df_r_scaler = self.robust_scaler(X)
        
        #X = self.feature_selector(X, y, 20)
 
        return X, test_df_r_scaler, ids


    #Redefinition der Methode robust_scaler()(wichtig : robust_scaler ist eine Methode der Klasse Preprocessing)
    def robust_scaler(self, df):
        
        df_r_scaler = RobustScaler()
        
        df_scaled =  df_r_scaler.fit_transform(df)
        df_scaled = pd.DataFrame(df_scaled, columns=df.columns) #In ein DataFrame mit den gleichen Spalten umwandeln, weil R_scaler gibt ein numy array zurück
        
        
        return df_scaled, df_r_scaler

import pandas as pd

test_df = pd.read_csv("test.csv")
test_df.head()

test_phase = Test(test_df)
X_test, test_df_r_scaler, ids  = test_phase.process()

X_test

#Lesen der gespeicherten Modelle aus der Festplatte
test_model_dict = {
    'RandomForest': joblib.load('RandomForest.pkl'),
    'baggingModel': joblib.load('bagging_model.pkl'),
    'KNN': joblib.load('KNN_regressor.pkl'),
    'votingModel': joblib.load('voting_model.pkl'),
    
}

list_prediction = test_phase.test(test_model_dict, X_test, test_df_r_scaler)

Test in process:  25%|██▌       | 1/4 [00:00<00:00,  8.97model/s]

************************* RANDOMFOREST ****************************
[[ 1.05510883e+02]
 [ 1.45149285e+03]
 [-6.92630444e-02]
 ...
 [ 3.42114961e+02]
 [ 1.28330564e+04]
 [ 1.18808062e+01]]

****************************************************************************************************************************************
************************* BAGGINGMODEL ****************************

Test in process:  50%|█████     | 2/4 [00:00<00:01,  1.78model/s]

[[1.06267484e+02]
 [1.44855759e+03]
 [4.76299157e-02]
 ...
 [3.48101929e+02]
 [1.28329333e+04]
 [1.20982205e+01]]

****************************************************************************************************************************************
************************* KNN ****************************

Test in process:  75%|███████▌  | 3/4 [00:01<00:00,  2.40model/s]

[[ 1.03785727e+02]
 [ 1.43600541e+03]
 [-1.24869425e-01]
 ...
 [ 3.14189143e+02]
 [ 1.71584194e+04]
 [ 1.02487491e+01]]

****************************************************************************************************************************************
************************* VOTINGMODEL ****************************

Test in process: 100%|██████████| 4/4 [00:02<00:00,  1.72model/s]

[[ 1.07463654e+02]
 [ 1.51975673e+03]
 [-1.30197477e-01]
 ...
 [ 3.67020248e+02]
 [ 1.33278466e+04]
 [ 1.09833060e+01]]

****************************************************************************************************************************************

# Die Vorhersagen von RandomForest wird hier als die Besten betrachtet
best_prediction = list_prediction[0]
best_prediction = pd.DataFrame(best_prediction, columns=["Next Week's Deaths"])
best_prediction

ids = pd.DataFrame(ids, columns=['Id'])
ids

covid_vorhersage = ids.copy()
covid_vorhersage["Next Week's Deaths"] = best_prediction["Next Week's Deaths"]
covid_vorhersage

covid_vorhersage.to_csv("covid_vorhersage.csv", index=False)  # index=False to avoid saving indexes
covid_vorhersage.to_excel("covid_vorhersage.xlsx", index=False)
print("CSV DATEI GESPEICHERT.")
print("XLSX DATEI GESPEICHERT.")

CSV DATEI GESPEICHERT.
XLSX DATEI GESPEICHERT.

for name, model in model_dict.items():
    print(f"******************{name.upper()}*******************")
    print(model.get_params())

******************KNN*******************
{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': None, 'n_neighbors': 5, 'p': 2, 'weights': 'uniform'}
******************SVR*******************
{'C': 1.0, 'cache_size': 200, 'coef0': 0.0, 'degree': 3, 'epsilon': 0.1, 'gamma': 'scale', 'kernel': 'rbf', 'max_iter': -1, 'shrinking': True, 'tol': 0.001, 'verbose': False}
******************DECISIONTREE*******************
{'ccp_alpha': 0.0, 'criterion': 'squared_error', 'max_depth': None, 'max_features': None, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': 0, 'splitter': 'best'}
******************LREGRESSOR*******************
{'copy_X': True, 'fit_intercept': True, 'n_jobs': None, 'positive': False}
******************RANDOMFOREST*******************
{'bootstrap': True, 'ccp_alpha': 0.0, 'criterion': 'squared_error', 'max_depth': None, 'max_features': 1.0, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 0, 'verbose': 0, 'warm_start': False}

	Id	Weekly Cases	Year	Weekly Cases per Million	Weekly Deaths	Weekly Deaths per Million	Total Vaccinations	People Vaccinated	People Fully Vaccinated	Total Boosters	Daily Vaccinations	Total Vaccinations per Hundred	People Vaccinated per Hundred	People Fully Vaccinated per Hundred	Total Boosters per Hundred	Daily Vaccinations per Hundred	Daily People Vaccinated	Daily People Vaccinated per Hundred	Next Week's Deaths
count	1.291560e+05	1.289430e+05	129156.000000	128288.000000	127898.000000	127247.000000	4.027000e+04	3.842900e+04	3.657400e+04	1.984700e+04	7.784000e+04	40270.000000	38429.000000	36574.000000	19847.000000	77840.000000	7.739100e+04	77391.000000	129156.000000
mean	5.502597e+08	9.520131e+04	2020.912919	1379.071563	1072.815494	10.845384	2.450093e+08	1.174096e+08	9.862264e+07	4.365458e+07	4.742138e+05	89.723652	43.424276	38.421866	20.160324	2702.988798	1.823430e+05	0.114669	1064.082776
std	2.599890e+08	6.329716e+05	0.739667	4013.421702	5287.848128	24.740908	1.032824e+09	4.932070e+08	4.303844e+08	1.862997e+08	2.593336e+06	74.209648	30.074617	29.197973	22.598973	3468.942102	1.061855e+06	0.202150	5251.447471
min	1.000006e+08	0.000000e+00	2020.000000	0.000000	0.000000	0.000000	0.000000e+00	0.000000e+00	1.000000e+00	1.000000e+00	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.000000	0.000000
25%	3.253421e+08	1.040000e+02	2020.000000	18.291500	1.000000	0.050000	9.900038e+05	5.771390e+05	4.683338e+05	3.894150e+04	1.065000e+03	18.730000	12.810000	8.390000	0.295000	402.000000	3.280000e+02	0.012000	1.000000
50%	5.497285e+08	1.084000e+03	2021.000000	188.584000	13.000000	1.709000	7.772026e+06	4.306986e+06	3.811594e+06	1.397130e+06	9.182500e+03	79.195000	46.330000	38.515000	9.780000	1498.000000	3.287000e+03	0.046000	13.000000
75%	7.756598e+08	9.750000e+03	2021.000000	1046.367750	127.000000	10.934000	4.976308e+07	2.557442e+07	2.168825e+07	1.114840e+07	6.394475e+04	146.290000	70.380000	64.720000	36.860000	3840.000000	2.318000e+04	0.141000	125.000000
max	9.999993e+08	2.406618e+07	2022.000000	104220.239000	103568.000000	1040.710000	1.212140e+10	5.255161e+09	4.816606e+09	2.129044e+09	4.368841e+07	366.870000	128.780000	126.790000	125.850000	117862.000000	2.099974e+07	11.786000	102123.000000

	Id	Location	Weekly Cases	Year	Weekly Cases per Million	Weekly Deaths	Weekly Deaths per Million	Total Vaccinations	People Vaccinated	People Fully Vaccinated	Total Boosters	Daily Vaccinations	Total Vaccinations per Hundred	People Vaccinated per Hundred	People Fully Vaccinated per Hundred	Daily Vaccinations per Hundred	Daily People Vaccinated	Daily People Vaccinated per Hundred	Next Week's Deaths
241	275164452	World	4174523.0	2020	527.800	77527.0	9.802	11875406.0	7231498.0	44680.0	1.0	897447.0	0.15	0.09	0.00	113.0	690726.0	0.009	81042.0
242	857254713	World	4424216.0	2021	559.369	79456.0	10.046	13722790.0	9050886.0	58460.0	9.0	1079269.0	0.17	0.11	0.00	136.0	735617.0	0.009	92754.0
243	515683834	World	4553174.0	2021	575.674	80332.0	10.157	17002186.0	11343354.0	191881.0	15.0	1303377.0	0.21	0.14	0.00	165.0	851085.0	0.011	94477.0
244	725478352	World	4619286.0	2021	584.033	79640.0	10.069	18569106.0	12578084.0	366880.0	23.0	1397939.0	0.23	0.16	0.00	177.0	845521.0	0.011	96212.0
245	844503137	World	4649535.0	2021	587.857	81042.0	10.246	20361402.0	14002427.0	650359.0	27.0	1581369.0	0.26	0.18	0.01	200.0	928498.0	0.012	96742.0

	CONTINENT	SQMI	SQKM	continent_geometry
0	Africa	1.158346e+07	3.000115e+07	MULTIPOLYGON (((35.48832 -21.685, 35.45221 -21...
1	Antarctica	4.754809e+06	1.231495e+07	MULTIPOLYGON (((-60.22 -80.28585, -60.23361 -8...
2	Asia	1.731728e+07	4.485173e+07	MULTIPOLYGON (((150.8936 -10.64874, 150.88053 ...
3	Australia	2.973612e+06	7.701651e+06	MULTIPOLYGON (((158.88218 -54.71139, 158.87967...
4	Europe	3.821854e+06	9.898597e+06	MULTIPOLYGON (((23.84853 35.52298, 23.97138 35...
5	North America	9.339528e+06	2.418936e+07	MULTIPOLYGON (((-81.67847 7.38861, -81.64945 7...
6	Oceania	1.656787e+05	4.291076e+05	MULTIPOLYGON (((169.18552 -52.57695, 169.16165...
7	South America	6.856255e+06	1.775769e+07	MULTIPOLYGON (((-67.2089 -55.89104, -67.24695 ...

	featurecla	scalerank	LABELRANK	SOVEREIGNT	SOV_A3	LEVEL	TYPE	TLC	ADMIN	ADM0_A3	GEOUNIT	GU_A3	SUBUNIT	SU_A3	BRK_DIFF	NAME	NAME_LONG	BRK_A3	BRK_NAME	BRK_GROUP	ABBREV	POSTAL	FORMAL_EN	FORMAL_FR	NAME_CIAWF	NOTE_ADM0	NOTE_BRK	NAME_SORT	NAME_ALT	MAPCOLOR7	MAPCOLOR8	MAPCOLOR9	MAPCOLOR13	POP_EST	POP_RANK	POP_YEAR	GDP_MD	GDP_YEAR	ECONOMY	INCOME_GRP	FIPS_10	ISO_A2	ISO_A2_EH	ISO_A3	ISO_A3_EH	ISO_N3	ISO_N3_EH	UN_A3	WB_A2	WB_A3	WOE_ID	WOE_ID_EH	...	NAME_IT	NAME_JA	NAME_KO	NAME_NL	NAME_PL	NAME_PT	NAME_RU	NAME_SV	NAME_TR	NAME_UK	NAME_UR	NAME_VI	NAME_ZH	NAME_ZHT	FCLASS_ISO	TLC_DIFF	FCLASS_TLC	FCLASS_US	FCLASS_FR	FCLASS_RU	FCLASS_ES	FCLASS_CN	FCLASS_TW	FCLASS_IN	FCLASS_NP	FCLASS_PK	FCLASS_DE	FCLASS_GB	FCLASS_BR	FCLASS_IL	FCLASS_PS	FCLASS_SA	FCLASS_EG	FCLASS_MA	FCLASS_PT	FCLASS_AR	FCLASS_JP	FCLASS_KO	FCLASS_VN	FCLASS_TR	FCLASS_ID	FCLASS_PL	FCLASS_GR	FCLASS_IT	FCLASS_NL	FCLASS_SE	FCLASS_BD	FCLASS_UA	geometry	Location	Weekly Cases	CONTINENT_y	SQMI	SQKM	continent_geometry
0	Admin-0 country	1.0	6.0	Fiji	FJI	2.0	Sovereign country	1	Fiji	FJI	Fiji	FJI	Fiji	FJI	0.0	Fiji	Fiji	FJI	Fiji	None	Fiji	FJ	Republic of Fiji	None	Fiji	None	None	Fiji	None	5.0	1.0	2.0	2.0	889953.0	11.0	2019.0	5496.0	2019.0	6. Developing region	4. Lower middle income	FJ	FJ	FJ	FJI	FJI	242	242	242	FJ	FJI	23424813.0	23424813.0	...	Figi	フィジー	피지	Fiji	Fidżi	Fiji	Фиджи	Fiji	Fiji	Фіджі	فجی	Fiji	斐济	斐濟	Admin-0 country	None	Admin-0 country	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	MULTIPOLYGON (((180 -16.06713, 180 -16.55522, ...	Fiji	372397.0	NaN	NaN	NaN	None
1	Admin-0 country	1.0	3.0	United Republic of Tanzania	TZA	2.0	Sovereign country	1	United Republic of Tanzania	TZA	Tanzania	TZA	Tanzania	TZA	0.0	Tanzania	Tanzania	TZA	Tanzania	None	Tanz.	TZ	United Republic of Tanzania	None	Tanzania	None	None	Tanzania	None	3.0	6.0	2.0	2.0	58005463.0	16.0	2019.0	63177.0	2019.0	7. Least developed region	5. Low income	TZ	TZ	TZ	TZA	TZA	834	834	834	TZ	TZA	23424973.0	23424973.0	...	Tanzania	タンザニア	탄자니아	Tanzania	Tanzania	Tanzânia	Танзания	Tanzania	Tanzanya	Танзанія	تنزانیہ	Tanzania	坦桑尼亚	坦尚尼亞	Admin-0 country	None	Admin-0 country	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	POLYGON ((33.90371 -0.95, 34.07262 -1.05982, 3...	Tanzania	205890.0	NaN	NaN	NaN	None
2	Admin-0 country	1.0	7.0	Western Sahara	SAH	2.0	Indeterminate	1	Western Sahara	SAH	Western Sahara	SAH	Western Sahara	SAH	1.0	W. Sahara	Western Sahara	B28	W. Sahara	None	W. Sah.	WS	Sahrawi Arab Democratic Republic	None	Western Sahara	None	Self admin.; Claimed by Morocco	Western Sahara	None	4.0	7.0	4.0	4.0	603253.0	11.0	2017.0	907.0	2007.0	7. Least developed region	5. Low income	WI	EH	EH	ESH	ESH	732	732	732	-99	-99	23424990.0	23424990.0	...	Sahara Occidentale	西サハラ	서사하라	Westelijke Sahara	Sahara Zachodnia	Sara Ocidental	Западная Сахара	Västsahara	Batı Sahra	Західна Сахара	مغربی صحارا	Tây Sahara	西撒哈拉	西撒哈拉	Admin-0 dependency	None	Admin-0 dependency	None	Unrecognized	None	None	None	None	Unrecognized	None	None	None	None	None	None	Unrecognized	Unrecognized	None	Unrecognized	None	None	None	None	None	Unrecognized	Unrecognized	Unrecognized	None	None	Unrecognized	None	None	None	POLYGON ((-8.66559 27.65643, -8.66512 27.58948...	Western Sahara	NaN	NaN	NaN	NaN	None

	featurecla	scalerank	LABELRANK	SOVEREIGNT	SOV_A3	LEVEL	TYPE	TLC	ADMIN	ADM0_A3	GEOUNIT	GU_A3	SUBUNIT	SU_A3	BRK_DIFF	NAME	NAME_LONG	BRK_A3	BRK_NAME	BRK_GROUP	ABBREV	POSTAL	FORMAL_EN	FORMAL_FR	NAME_CIAWF	NOTE_ADM0	NOTE_BRK	NAME_SORT	NAME_ALT	MAPCOLOR7	MAPCOLOR8	MAPCOLOR9	MAPCOLOR13	POP_EST	POP_RANK	POP_YEAR	GDP_MD	GDP_YEAR	ECONOMY	INCOME_GRP	FIPS_10	ISO_A2	ISO_A2_EH	ISO_A3	ISO_A3_EH	ISO_N3	ISO_N3_EH	UN_A3	WB_A2	WB_A3	WOE_ID	WOE_ID_EH	...	NAME_JA	NAME_KO	NAME_NL	NAME_PL	NAME_PT	NAME_RU	NAME_SV	NAME_TR	NAME_UK	NAME_UR	NAME_VI	NAME_ZH	NAME_ZHT	FCLASS_ISO	TLC_DIFF	FCLASS_TLC	FCLASS_US	FCLASS_FR	FCLASS_RU	FCLASS_ES	FCLASS_CN	FCLASS_TW	FCLASS_IN	FCLASS_NP	FCLASS_PK	FCLASS_DE	FCLASS_GB	FCLASS_BR	FCLASS_IL	FCLASS_PS	FCLASS_SA	FCLASS_EG	FCLASS_MA	FCLASS_PT	FCLASS_AR	FCLASS_JP	FCLASS_KO	FCLASS_VN	FCLASS_TR	FCLASS_ID	FCLASS_PL	FCLASS_GR	FCLASS_IT	FCLASS_NL	FCLASS_SE	FCLASS_BD	FCLASS_UA	geometry	Location	Weekly Cases	CONTINENT_y	SQMI	SQKM	continent_geometry	Weekly Deaths
0	Admin-0 country	1.0	6.0	Fiji	FJI	2.0	Sovereign country	1	Fiji	FJI	Fiji	FJI	Fiji	FJI	0.0	Fiji	Fiji	FJI	Fiji	None	Fiji	FJ	Republic of Fiji	None	Fiji	None	None	Fiji	None	5.0	1.0	2.0	2.0	889953.0	11.0	2019.0	5496.0	2019.0	6. Developing region	4. Lower middle income	FJ	FJ	FJ	FJI	FJI	242	242	242	FJ	FJI	23424813.0	23424813.0	...	フィジー	피지	Fiji	Fidżi	Fiji	Фиджи	Fiji	Fiji	Фіджі	فجی	Fiji	斐济	斐濟	Admin-0 country	None	Admin-0 country	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	MULTIPOLYGON (((180 -16.06713, 180 -16.55522, ...	Fiji	372397.0	NaN	NaN	NaN	None	4754.0
1	Admin-0 country	1.0	3.0	United Republic of Tanzania	TZA	2.0	Sovereign country	1	United Republic of Tanzania	TZA	Tanzania	TZA	Tanzania	TZA	0.0	Tanzania	Tanzania	TZA	Tanzania	None	Tanz.	TZ	United Republic of Tanzania	None	Tanzania	None	None	Tanzania	None	3.0	6.0	2.0	2.0	58005463.0	16.0	2019.0	63177.0	2019.0	7. Least developed region	5. Low income	TZ	TZ	TZ	TZA	TZA	834	834	834	TZ	TZA	23424973.0	23424973.0	...	タンザニア	탄자니아	Tanzania	Tanzania	Tanzânia	Танзания	Tanzania	Tanzanya	Танзанія	تنزانیہ	Tanzania	坦桑尼亚	坦尚尼亞	Admin-0 country	None	Admin-0 country	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	POLYGON ((33.90371 -0.95, 34.07262 -1.05982, 3...	Tanzania	205890.0	NaN	NaN	NaN	None	4159.0
2	Admin-0 country	1.0	7.0	Western Sahara	SAH	2.0	Indeterminate	1	Western Sahara	SAH	Western Sahara	SAH	Western Sahara	SAH	1.0	W. Sahara	Western Sahara	B28	W. Sahara	None	W. Sah.	WS	Sahrawi Arab Democratic Republic	None	Western Sahara	None	Self admin.; Claimed by Morocco	Western Sahara	None	4.0	7.0	4.0	4.0	603253.0	11.0	2017.0	907.0	2007.0	7. Least developed region	5. Low income	WI	EH	EH	ESH	ESH	732	732	732	-99	-99	23424990.0	23424990.0	...	西サハラ	서사하라	Westelijke Sahara	Sahara Zachodnia	Sara Ocidental	Западная Сахара	Västsahara	Batı Sahra	Західна Сахара	مغربی صحارا	Tây Sahara	西撒哈拉	西撒哈拉	Admin-0 dependency	None	Admin-0 dependency	None	Unrecognized	None	None	None	None	Unrecognized	None	None	None	None	None	None	Unrecognized	Unrecognized	None	Unrecognized	None	None	None	None	None	Unrecognized	Unrecognized	Unrecognized	None	None	Unrecognized	None	None	None	POLYGON ((-8.66559 27.65643, -8.66512 27.58948...	Western Sahara	NaN	NaN	NaN	NaN	None	NaN

	Id	Location	Weekly Cases	Year	Weekly Cases per Million	Weekly Deaths	Weekly Deaths per Million	Total Vaccinations	People Vaccinated	People Fully Vaccinated	Total Boosters	Daily Vaccinations	Total Vaccinations per Hundred	People Vaccinated per Hundred	People Fully Vaccinated per Hundred	Total Boosters per Hundred	Daily Vaccinations per Hundred	Daily People Vaccinated	Daily People Vaccinated per Hundred	Next Week's Deaths
0	911530868	World	2372.0	2020	0.300	65.0	0.008	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	344.0
1	807936902	World	5023.0	2020	0.635	114.0	0.014	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	361.0
2	773590408	World	5612.0	2020	0.710	116.0	0.015	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	431.0
3	130466459	World	7580.0	2020	0.958	153.0	0.019	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	463.0
4	544040446	World	8983.0	2020	1.136	187.0	0.024	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	506.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
129151	541829605	Zimbabwe	464.0	2022	29.012	13.0	0.813	11949993.0	6297324.0	4601845.0	1050824.0	5665.0	74.72	39.37	28.77	6.57	354.0	1427.0	0.009	9.0
129152	969939474	Zimbabwe	471.0	2022	29.449	12.0	0.750	11958771.0	6299348.0	4605821.0	1053602.0	5295.0	74.77	39.39	28.80	6.59	331.0	1362.0	0.009	7.0
129153	667902340	Zimbabwe	450.0	2022	28.136	13.0	0.813	NaN	NaN	NaN	NaN	5316.0	NaN	NaN	NaN	NaN	332.0	1483.0	0.009	5.0
129154	961193720	Zimbabwe	277.0	2022	17.320	6.0	0.375	11974313.0	6305470.0	4611113.0	1057730.0	5358.0	74.87	39.43	28.83	6.61	335.0	1633.0	0.010	7.0
129155	832612563	Zimbabwe	277.0	2022	17.320	6.0	0.375	11984914.0	6310089.0	4614738.0	1060087.0	6190.0	74.94	39.45	28.85	6.63	387.0	2102.0	0.013	8.0

	Weekly Cases	Weekly Deaths
Year
2020	1.769078e+09	40011144.0
2021	4.385636e+09	77041928.0
2022	6.120829e+09	20157884.0

	Id	Weekly Cases	Year	Weekly Cases per Million	Weekly Deaths	Weekly Deaths per Million	Total Vaccinations	People Vaccinated	People Fully Vaccinated	Total Boosters	Daily Vaccinations	Total Vaccinations per Hundred	People Vaccinated per Hundred	People Fully Vaccinated per Hundred	Total Boosters per Hundred	Daily Vaccinations per Hundred	Daily People Vaccinated	Daily People Vaccinated per Hundred	Next Week's Deaths
Location
World	343175836065	2.861184e+09	1311529	361749.577	32099475.0	4058.447	2.496880e+12	1.218746e+12	9.768278e+11	2.361440e+11	8.928484e+09	31568.98	15409.02	12350.41	2985.62	1128858.0	3.373729e+09	42.648	32062228.0
Asia	367764316423	8.042532e+08	1345872	171364.344	7623339.0	1624.324	1.687875e+12	8.131908e+11	6.406760e+11	1.433659e+11	6.305201e+09	35963.95	17326.78	13650.98	3054.77	1343473.0	2.273782e+09	48.449	7631107.0
Upper middle income	374107922156	7.087520e+08	1370119	283338.992	13535982.0	5411.303	1.159670e+12	5.135934e+11	4.360799e+11	1.257807e+11	3.953455e+09	46360.36	20532.02	17433.19	5028.34	1580471.0	1.132104e+09	45.255	13563349.0
China	374341050981	3.989379e+06	1362031	2797.793	20054.0	14.068	7.607777e+11	2.963705e+10	3.743798e+10	1.161732e+10	2.540583e+09	53354.45	2078.48	2625.60	814.74	1781743.0	4.908809e+08	34.387	18632.0
Lower middle income	367483703569	4.694772e+08	1341833	139592.556	6832004.0	2031.397	7.548071e+11	4.314895e+11	3.023138e+11	2.416828e+10	3.256393e+09	22443.17	12829.73	8988.91	718.60	968251.0	1.607126e+09	47.796	6843420.0
High income	363980741314	1.709065e+09	1351928	1376752.513	13073223.0	10531.262	5.792576e+11	2.708835e+11	2.325204e+11	8.212733e+10	1.862288e+09	46662.60	21821.33	18730.84	6615.81	1500177.0	7.227329e+08	58.212	13044838.0
India	335873742187	2.337880e+08	1242831	166094.079	2685113.0	1907.633	3.479345e+11	2.088908e+11	1.362487e+11	2.793464e+09	1.401385e+09	24718.99	14840.59	9679.71	198.44	995613.0	7.312434e+08	51.945	2750931.0
Europe	346698622651	1.063511e+09	1295347	1420561.169	9271854.0	12384.673	2.984635e+11	1.390928e+11	1.221195e+11	4.154054e+10	9.091805e+08	39866.58	18579.04	16311.82	5548.60	1214416.0	3.529631e+08	47.139	9305360.0
North America	377356288305	5.375706e+08	1372117	901487.252	7663774.0	12851.878	2.449427e+11	1.215372e+11	1.008609e+11	2.424261e+10	7.630040e+08	41076.12	20381.34	16913.89	4065.25	1279537.0	3.215156e+08	53.905	7668808.0
European Union	374811896567	8.099883e+08	1358001	1809651.343	5861020.0	13094.524	2.201179e+11	1.005995e+11	8.945689e+10	3.451759e+10	6.553287e+08	49178.11	22475.62	19986.20	7711.81	1464123.0	2.465415e+08	55.078	5881284.0
South America	352741386470	3.075180e+08	1271115	708637.456	6587571.0	15180.268	1.724596e+11	8.740477e+10	6.713583e+10	2.070666e+10	6.501973e+08	39741.16	20141.32	15470.61	4771.61	1498307.0	2.721470e+08	62.717	6598912.0
United States	359400382866	4.678046e+08	1331739	1388153.889	5353637.0	15886.282	1.624920e+11	8.066272e+10	6.772700e+10	1.721284e+10	4.618832e+08	48942.06	24295.37	20399.23	5184.13	1391187.0	2.007561e+08	60.470	5395803.0
Africa	355472509106	6.303815e+07	1325685	45273.202	1329147.0	954.589	8.792232e+10	5.492479e+10	3.838919e+10	2.443066e+09	4.322672e+08	6314.50	3944.53	2757.04	175.47	310449.0	2.639947e+08	18.956	1327422.0
Brazil	346312487594	1.662298e+08	1252936	775592.243	3504680.0	16352.089	8.222781e+10	4.225591e+10	3.141502e+10	9.414485e+09	3.345943e+08	38365.75	19715.74	14657.64	4392.59	1561146.0	1.388454e+08	64.787	3498361.0
Japan	365314941643	4.872596e+07	1341839	391019.721	164936.0	1323.557	5.721746e+10	2.636771e+10	2.427828e+10	6.571470e+09	2.129528e+08	45916.23	21159.84	19483.06	5273.59	1708922.0	7.811961e+07	62.689	166149.0
Indonesia	362213171537	3.231596e+07	1305473	118047.785	846050.0	3090.564	5.080258e+10	3.632436e+10	2.568472e+10	1.795383e+09	3.261263e+08	18557.70	13269.16	9382.35	655.74	1191302.0	1.570545e+08	57.369	844727.0

	Id	Location	Weekly Cases	Year	Weekly Cases per Million	Weekly Deaths	Weekly Deaths per Million	Total Vaccinations	People Vaccinated	People Fully Vaccinated	Total Boosters	Daily Vaccinations	Total Vaccinations per Hundred	People Vaccinated per Hundred	People Fully Vaccinated per Hundred	Total Boosters per Hundred	Daily Vaccinations per Hundred	Daily People Vaccinated	Daily People Vaccinated per Hundred	Next Week's Deaths
0	322402222	Antigua and Barbuda	6.0	2020	64.364	2.0	21.455	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0
1	572578633	Antigua and Barbuda	8.0	2020	85.818	2.0	21.455	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0
2	852095863	Antigua and Barbuda	4.0	2020	42.909	1.0	10.727	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0
3	649506512	Antigua and Barbuda	4.0	2020	42.909	0.0	0.000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0
4	949219283	Antigua and Barbuda	4.0	2020	42.909	1.0	10.727	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0

	Id	Location	Weekly Cases	Year	Weekly Cases per Million	Weekly Deaths	Weekly Deaths per Million	Total Vaccinations	People Vaccinated	People Fully Vaccinated	Total Boosters	Daily Vaccinations	Total Vaccinations per Hundred	People Vaccinated per Hundred	People Fully Vaccinated per Hundred	Total Boosters per Hundred	Daily Vaccinations per Hundred	Daily People Vaccinated	Daily People Vaccinated per Hundred	Next Week's Deaths
0	955039050	Andorra	361.0	2021	4567.654	1.0	12.653	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0
1	193148502	Andorra	360.0	2021	4555.002	1.0	12.653	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0
2	363805587	Andorra	371.0	2021	4694.182	1.0	12.653	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0
3	210086497	Andorra	389.0	2021	4921.932	0.0	0.000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.0
4	733229232	Andorra	365.0	2021	4618.266	0.0	0.000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3.0

	Id	Location	Weekly Cases	Year	Weekly Cases per Million	Weekly Deaths	Weekly Deaths per Million	Total Vaccinations	People Vaccinated	People Fully Vaccinated	Total Boosters	Daily Vaccinations	Total Vaccinations per Hundred	People Vaccinated per Hundred	People Fully Vaccinated per Hundred	Total Boosters per Hundred	Daily Vaccinations per Hundred	Daily People Vaccinated	Daily People Vaccinated per Hundred	Next Week's Deaths
0	984724956	Andorra	2010.0	2022	25432.093	1.0	12.653	NaN	NaN	NaN	NaN	758.0	NaN	NaN	NaN	NaN	9591.0	27.0	0.034	1.0
1	888965535	Andorra	2170.0	2022	27456.538	1.0	12.653	NaN	NaN	NaN	NaN	865.0	NaN	NaN	NaN	NaN	10945.0	27.0	0.034	1.0
2	240962892	Andorra	2466.0	2022	31201.761	1.0	12.653	NaN	NaN	NaN	NaN	865.0	NaN	NaN	NaN	NaN	10945.0	27.0	0.034	0.0
3	253613407	Andorra	2167.0	2022	27418.579	1.0	12.653	NaN	NaN	NaN	NaN	865.0	NaN	NaN	NaN	NaN	10945.0	27.0	0.034	0.0
4	263432761	Andorra	2668.0	2022	33757.623	1.0	12.653	NaN	NaN	NaN	NaN	865.0	NaN	NaN	NaN	NaN	10945.0	27.0	0.034	1.0

	Id	Location	Weekly Cases	Year	Weekly Cases per Million	Weekly Deaths	Weekly Deaths per Million	Total Vaccinations	People Vaccinated	People Fully Vaccinated	Total Boosters	Daily Vaccinations	Total Vaccinations per Hundred	People Vaccinated per Hundred	People Fully Vaccinated per Hundred	Total Boosters per Hundred	Daily Vaccinations per Hundred	Daily People Vaccinated	Daily People Vaccinated per Hundred	Change in Weekly Cases	Change in Weekly Deaths
0	-0.585821	-0.128480	-0.037661	0.0	0.623875	-0.220794	0.235415	-0.368344	-0.467034	-0.372089	0.102753	-0.655627	0.577877	0.395524	0.407971	0.506547	-0.599942	-0.648269	-0.275791	-0.000681	0.000000
1	-0.524440	-0.613165	-0.916861	0.0	-1.477418	-0.787966	-0.964828	0.031686	-0.000489	0.053349	0.240047	-0.271063	0.460992	0.476912	0.377903	0.367340	-0.447614	-0.436750	-0.302928	-0.664669	-0.410260
2	0.239260	0.288518	-0.640348	-1.0	-1.362314	-0.502756	-0.910295	-0.703450	-0.538985	-1.000148	-2.243335	0.062476	-3.480501	-4.032284	-4.696751	-0.685921	-0.199723	0.366111	0.772656	0.208147	0.206305
3	0.190797	0.612938	0.884800	-1.0	-0.295497	1.240838	0.269707	-0.382701	-0.899847	-4.293622	-2.001856	0.513833	-6.047904	-8.091639	-5.974225	-0.685921	-1.592151	0.357599	-0.366561	1.151139	1.261218
4	-0.227316	-0.148290	-0.087805	0.0	-0.657244	0.165473	-0.299145	0.520060	0.510786	0.531950	0.491690	0.057072	0.260589	0.176432	0.106625	0.323765	-0.634931	0.085567	-0.212785	-0.735210	-0.777858

	Id	Location	Weekly Cases	Year	Weekly Cases per Million	Weekly Deaths	Weekly Deaths per Million	Total Vaccinations	People Vaccinated	People Fully Vaccinated	Total Boosters	Daily Vaccinations	Total Vaccinations per Hundred	People Vaccinated per Hundred	People Fully Vaccinated per Hundred	Total Boosters per Hundred	Daily Vaccinations per Hundred	Daily People Vaccinated	Daily People Vaccinated per Hundred	Change in Weekly Cases	Change in Weekly Deaths
0	0.596986	-0.492328	0.979649	-1.0	0.319766	1.285056	0.963111	-0.540310	-0.329897	-1.072018	-1.854039	0.428265	-5.981057	-7.432045	-6.338454	-0.695839	-1.041286	0.743761	0.027133	0.000035	0.000000
1	-1.242596	0.288518	-0.530639	0.0	-0.356750	-0.179400	0.240976	-0.339854	-0.435509	-0.299885	0.113072	-1.011875	0.571539	0.358889	0.312561	0.488151	-1.539455	-1.026895	-0.378852	-1.131724	-1.100185
2	-1.206062	0.294906	-0.767186	-1.0	-1.638385	-0.624341	-0.991584	-0.828157	-0.664786	-1.086853	-2.191878	-0.015337	-4.138867	-4.920713	-5.466222	-0.695839	-0.396131	0.330038	0.647543	-0.177225	-0.334265
3	-0.462574	0.490516	0.811217	-1.0	0.242377	1.253682	1.174724	0.325344	0.466014	0.330110	-1.498679	0.775537	-2.682443	-3.266519	-3.340602	-0.695839	0.141841	0.908834	1.077399	1.182838	1.410880
4	-0.180188	0.458711	-0.047910	0.0	0.508739	-0.477714	-0.358206	-0.270373	-0.384608	-0.233330	0.164356	-0.609075	0.843306	0.629648	0.606726	0.572601	-0.561552	-0.821929	-0.350610	-0.643766	-1.300725

	Id	Location	Weekly Cases	Year	Weekly Cases per Million	Weekly Deaths	Weekly Deaths per Million	Total Vaccinations	People Vaccinated	People Fully Vaccinated	Total Boosters	Daily Vaccinations	Total Vaccinations per Hundred	People Vaccinated per Hundred	People Fully Vaccinated per Hundred	Total Boosters per Hundred	Daily Vaccinations per Hundred	Daily People Vaccinated	Daily People Vaccinated per Hundred
0	719557359	Monaco	185.0	2021	5042.796	0.0	0.00	NaN	NaN	NaN	NaN	158.0	NaN	NaN	NaN	NaN	4307.0	23.0	0.063
1	507527289	Liechtenstein	78.0	2020	1998.002	0.0	0.00	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	821551528	Albania	230.0	2022	80.569	1.0	0.35	NaN	NaN	NaN	NaN	1462.0	NaN	NaN	NaN	NaN	512.0	391.0	0.014
3	696602016	Niger	14.0	2022	0.554	1.0	0.04	NaN	NaN	NaN	NaN	72018.0	NaN	NaN	NaN	NaN	2852.0	60442.0	0.239
4	680387432	French Polynesia	1033.0	2022	3397.669	0.0	0.00	366984.0	186177.0	180807.0	NaN	22.0	120.71	61.24	59.47	NaN	72.0	22.0	0.007

	Id	Location	Weekly Cases	Year	Weekly Cases per Million	Weekly Deaths	Weekly Deaths per Million	Total Vaccinations	People Vaccinated	People Fully Vaccinated	Total Boosters	Daily Vaccinations	Total Vaccinations per Hundred	People Vaccinated per Hundred	People Fully Vaccinated per Hundred	Total Boosters per Hundred	Daily Vaccinations per Hundred	Daily People Vaccinated	Daily People Vaccinated per Hundred	Change in Weekly Cases	Change in Weekly Deaths
0	-1.634584	0.469428	0.186898	0.0	1.412112	-0.029017	1.085003	-0.802663	-0.880560	-0.761293	-0.176061	-0.864281	0.003449	-0.225067	-0.092652	0.296336	-0.628524	-0.980298	-0.327188	0.003773	0.000000
1	-0.826544	0.584921	0.471627	-1.0	0.370561	0.534240	0.446165	0.426122	0.451422	0.473237	0.345086	0.343477	-0.049973	-0.067670	-0.147440	0.087666	0.086093	0.340035	0.189086	0.225411	0.417222
2	0.606988	0.589646	-1.617883	0.0	-0.328736	-1.147918	-1.093027	-2.067439	-2.185036	-2.018231	-0.946753	-2.008936	0.131755	0.085528	0.108000	0.150845	-1.118003	-1.507219	-0.184249	-1.622740	-1.246025
3	0.130023	-3.964431	0.096595	0.0	-1.507476	0.562449	-0.679214	0.868243	1.003679	0.944487	0.376941	0.807095	-2.091770	-2.457100	-2.270112	-0.505914	-0.496687	0.965761	0.239977	1.338355	1.266920
4	-1.805496	-0.732684	-0.832521	-1.0	-0.120944	-0.827714	-0.312624	-1.113260	-1.145369	-1.023661	-0.829965	-0.879444	-0.082290	-0.006031	0.037740	-0.418379	-0.132745	-0.788337	-0.078505	-0.719468	-1.029736
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6171	0.006661	0.495718	0.264052	0.0	0.193502	-0.084225	-0.525849	0.460568	0.382710	0.456069	0.549975	-0.235357	0.776282	0.517459	0.535745	0.560944	-1.047233	-0.698768	-0.372324	0.350397	0.073704
6172	-0.252339	0.464084	0.076095	-1.0	0.803245	0.337578	1.425608	-1.436394	-1.373290	-1.503516	-2.187556	-0.435636	-3.798613	-4.597111	-4.521689	-0.712987	-0.148557	-0.160824	0.490230	-0.142536	0.312442
6173	-0.131281	0.294906	0.608054	0.0	0.899515	0.213560	0.081639	0.274570	0.223003	0.290489	0.402823	-0.096322	0.220077	-0.070711	0.013017	0.363134	-0.607518	-0.109239	-0.237587	0.417860	-0.091864
6174	-0.026174	0.617531	0.753401	-1.0	-0.545681	1.073568	-0.059163	1.632724	1.709303	1.705087	0.845696	1.395490	0.227496	0.275930	0.256214	-0.061598	0.602377	1.104047	0.273746	0.116914	0.637034
6175	-0.373429	0.431396	-0.385660	0.0	-0.858776	-0.555471	-0.940006	0.216466	0.187598	0.249146	0.328762	0.065612	0.276387	0.151129	0.153537	0.303840	-0.072697	-0.006702	-0.104826	-0.882893	-1.206679

	Next Week's Deaths
0	-0.182779
1	-0.570141
2	-0.440645
3	1.253895
4	0.152041

	Next Week's Deaths
0	1.287218
1	-0.130583
2	-0.455893
3	1.223960
4	-0.706320

	Next Week's Deaths
0	1.287218
1	-0.130583
2	-0.455893
3	1.223960
4	-0.706320

	Next Week's Deaths
0	105.510883
1	1451.492851
2	-0.069263
3	697.491272
4	3.784387
...	...
6171	96.532840
6172	599.035028
6173	342.114961
6174	12833.056432
6175	11.880806

	Id
0	140935772
1	277427273
2	922479531
3	618501440
4	122126258
...	...
6171	557746600
6172	448911171
6173	496850584
6174	542606568
6175	405586753

PREDICTION OF NEXT WEEK'S COVID 19 DEATHS BASED ON THIS WEEK'S DATA¶

SACKO Kalil, Master Student at University of applied Science Bochum, major : Computer Science¶

Einleitung¶

-------------------------------VORGEHENSWEISE-----------------------------¶

I. EXPLORATORY DATA ANALYSIS¶

Ziel :¶

Grundlegende Checkliste (nicht erschöpfend):¶

I-I. Grundlegende Analyse(Analyse der Datenstruktur)¶

I-II. Inhaltsanalyse:¶

I-III. Hypothese zum Testen:¶

Nullhypothesen(H0)¶

II. PRE-PROCESSING¶

Ziel:¶

Grundlegende Checkliste (nicht erschöpfend):¶

III. MODELLING AND TRAINING¶

Ziel:¶

Grundlegende Checkliste (nicht erschöpfend)::¶

IV. TESTPHASE¶

I. EXPLORATORY DATA ANALYSIS¶

I-I. Grundlegende Analyse(Analyse der Datenstruktur)¶

Anzahl der Zeilen und Spalten¶

Variablentypen¶

Descriptive Analyse¶

Asymmetrie:¶

Verteilungen um den Median:¶

Analyse fehlender Werte¶

---------------------------------------------------------------------------------------------------------------------¶

Analyse der Ausreisser(Outliers)¶

Analyse der Verteilung der Variablen¶

Interpretation der Skewness-Werte:¶

Allgemeine Schlussfolgerungen für die Variablen:¶

Asymmetrie nach rechts (Skewness > 0):¶

Sehr asymmetrische Spalten (Skewness > 7):¶

Leicht asymmetrische Spalten nach rechts (moderat hohe Skewness):¶

Asymmetrie nach links (Skewness < 0):¶

------------------------------------------------------------------------------------------------------------------------------¶

Probability plot¶

Achsen des Diagramms:¶

Abweichungen von der Linie:¶

Nach dieser Analyse:¶

I-II. Inhaltsanalyse:¶

Beziehungen zwischen unabhängigen Variablen und Zielvariable¶

Beziehungen zwischen unabhängigen Variablen¶

Zeitliche und geographische Analyse¶

Impfung und Auswirkungen¶

Vergleich der Wöchentlichen Todfälle einiger Orte mit den meisten Impfungsraten¶

I-III. Hypothese zum Testen:¶

Testvorbereitungs- und Testfunktionen¶

Anwendung von Testen für die Daten des Jahr 2020¶

Anwendung von Testen für die Daten des Jahr 2021¶

Anwendung von Testen für die Daten des Jahr 2022¶

Anwendung von Test(spearman) auf die Gesamten gereinigten Daten (2020-2022)¶

II. PRE-PROCESSING¶

Erstellung des Trainings- und Validierungs(Vortest)datensatzes¶

Encoding, Standardization, etc..¶

Verteilungsanzeige der Variablen bevor preprocessing¶

DEFINITION DER PREPROCESSING FUNKTIONEN¶

Verteilungsanzeige der Variablen nach Preprocessing¶

Bewertungs- und Diagnosenfunktion¶

Ertes Modell und Bewertung¶

III. MODELLING AND TRAINING¶

Bewertungs- und Optimimierungsklasse¶

Verschiedene Modelle zum Trainieren¶

Bewertung¶

Optimierung¶

Anwendung von ensemble Learners¶

The Voting¶

The Bagging¶

SPEICHERN DER BESTEN MODELLE AUF DIE FESTPLATTE¶

IV. TESTPHASE¶

DIE FINALE VORHERSAGE UND SUBMISSION FILE¶

ES GIBT NOCH RAUM FÜR DIE VERBESSERUNG¶

FAZIT¶